tirana-bert2bert-chatbot
Query: BERT2BERT dialogue response generation Indonesian
Results: 50
Date: 2026-07-08T07:34:43.322Z
1. Dependency Dialogue Acts – Annotation Scheme and Case Study
Authors: Jon Z. Cai, Brendan King, Margaret Perkoff, Shiran Dudy, Jie Cao, Marie Grace, Natalia Wojarnik, Ananya Ganesh, James H. Martin, Martha Palmer, Marilyn Walker, Jeffrey Flanigan
Categories: cs.CL, cs.AI
Published: 2023-02-25
arXiv: 2302.12944v1
Abstract:
In this paper, we introduce Dependency Dialogue Acts (DDA), a novel framework for capturing the structure of speaker-intentions in multi-party dialogues. DDA combines and adapts features from existing dialogue annotation frameworks, and emphasizes the multi-relational response structure of dialogues in addition to the dialogue acts and rhetorical relations. It represents the functional, discourse, and response structure in multi-party multi-threaded conversations. A few key features distinguish DDA from existing dialogue annotation frameworks such as SWBD-DAMSL and the ISO 24617-2 standard. First, DDA prioritizes the relational structure of the dialogue units and the dialog context, annotating both dialog acts and rhetorical relations as response relations to particular utterances. Second, DDA embraces overloading in dialogues, encouraging annotators to specify multiple response relations and dialog acts for each dialog unit. Lastly, DDA places an emphasis on adequately capturing how a speaker is using the full dialog context to plan and organize their speech. With these features, DDA is highly expressive and recall-oriented with regard to conversation dynamics between multiple speakers. In what follows, we present the DDA annotation framework and case studies annotating DDA structures in multi-party, multi-threaded conversations.
2. “No, they did not”: Dialogue response dynamics in pre-trained language models
Authors: Sanghee J. Kim, Lang Yu, Allyson Ettinger
Categories: cs.CL
Published: 2022-10-05
arXiv: 2210.02526v1
Abstract:
A critical component of competence in language is being able to identify relevant components of an utterance and reply appropriately. In this paper we examine the extent of such dialogue response sensitivity in pre-trained language models, conducting a series of experiments with a particular focus on sensitivity to dynamics involving phenomena of at-issueness and ellipsis. We find that models show clear sensitivity to a distinctive role of embedded clauses, and a general preference for responses that target main clause content of prior utterances. However, the results indicate mixed and generally weak trends with respect to capturing the full range of dynamics involved in targeting at-issue versus not-at-issue content. Additionally, models show fundamental limitations in grasp of the dynamics governing ellipsis, and response selections show clear interference from superficial factors that outweigh the influence of principled discourse constraints.
3. Controllable Mixed-Initiative Dialogue Generation through Prompting
Authors: Maximillian Chen, Xiao Yu, Weiyan Shi, Urvi Awasthi, Zhou Yu
Categories: cs.CL, cs.AI, cs.HC
Published: 2023-05-06
arXiv: 2305.04147v1
Abstract:
Mixed-initiative dialogue tasks involve repeated exchanges of information and conversational control. Conversational agents gain control by generating responses that follow particular dialogue intents or strategies, prescribed by a policy planner. The standard approach has been fine-tuning pre-trained language models to perform generation conditioned on these intents. However, these supervised generation models are limited by the cost and quality of data annotation. We instead prompt large language models as a drop-in replacement to fine-tuning on conditional generation. We formalize prompt construction for controllable mixed-initiative dialogue. Our findings show improvements over fine-tuning and ground truth responses according to human evaluation and automatic metrics for two tasks: PersuasionForGood and Emotional Support Conversations.
4. Controllable Generation of Dialogue Acts for Dialogue Systems via Few-Shot Response Generation and Ranking
Authors: Angela Ramirez, Karik Agarwal, Juraj Juraska, Utkarsh Garg, Marilyn A. Walker
Categories: cs.CL
Published: 2023-07-26
arXiv: 2307.14440v1
Abstract:
Dialogue systems need to produce responses that realize multiple types of dialogue acts (DAs) with high semantic fidelity. In the past, natural language generators (NLGs) for dialogue were trained on large parallel corpora that map from a domain-specific DA and its semantic attributes to an output utterance. Recent work shows that pretrained language models (LLMs) offer new possibilities for controllable NLG using prompt-based learning. Here we develop a novel few-shot overgenerate-and-rank approach that achieves the controlled generation of DAs. We compare eight few-shot prompt styles that include a novel method of generating from textual pseudo-references using a textual style transfer approach. We develop six automatic ranking functions that identify outputs with both the correct DA and high semantic accuracy at generation time. We test our approach on three domains and four LLMs. To our knowledge, this is the first work on NLG for dialogue that automatically ranks outputs using both DA and attribute accuracy. For completeness, we compare our results to fine-tuned few-shot models trained with 5 to 100 instances per DA. Our results show that several prompt settings achieve perfect DA accuracy, and near perfect semantic accuracy (99.81%) and perform better than few-shot fine-tuning.
5. Suryakala-Nusantara: Documenting Indonesian Sundials
Authors: Rhorom Priyatikanto
Categories: physics.pop-ph, astro-ph.IM
Published: 2013-12-10
arXiv: 1312.2742v1
Abstract:
Sundial is the ancient or classic timekeeper device, especially prior to the invention of mechanical clock. In the classical Islamic civilization, the daily movement of the Sun becomes main indicator of praying time, which can be deduced using sundial. This kind of device probably permeated to Indonesia during the Islamic acculturation. Since then, the development of astronomical knowledge, technology, art and architectural in classical Indonesia are partially reflected into sundial. These historical attractions of sundial demand comprehensive documentation and investigation of Indonesian sundial which are rarely found in the current literatures. The required spatial and temporal information regarding Indonesian sundial can be collected by general public through citizen science scheme. This concept may answer scientific curiosity of a research and also educate the people, expose them with science. In this article, general scheme of citizen science are discussed, its application for sundial study in Indonesia is proposed as Suryakala-Nusantara program.
6. Domain Adaptation of the Pyannote Diarization Pipeline for Conversational Indonesian Audio
Authors: Muhammad Daffa’i Rafi Prasetyo, Ramadhan Andika Putra, Zaidan Naufal Ilmi, Kurniawati Azizah
Categories: cs.SD
Published: 2026-01-07
arXiv: 2601.03684v1
Abstract:
This study presents a domain adaptation approach for speaker diarization targeting conversational Indonesian audio. We address the challenge of adapting an English-centric diarization pipeline to a low-resource language by employing synthetic data generation using neural Text-to-Speech technology. Experiments were conducted with varying training configurations, a small dataset (171 samples) and a large dataset containing 25 hours of synthetic speech. Results demonstrate that the baseline \texttt{pyannote/segmentation-3.0} model, trained on the AMI Corpus, achieves a Diarization Error Rate (DER) of 53.47% when applied zero-shot to Indonesian. Domain adaptation significantly improves performance, with the small dataset models reducing DER to 34.31% (1 epoch) and 34.81% (2 epochs). The model trained on the 25-hour dataset achieves the best performance with a DER of 29.24%, representing a 13.68% absolute improvement over the baseline while maintaining 99.06% Recall and 87.14% F1-Score.
7. Acknowledgment of Emotional States: Generating Validating Responses for Empathetic Dialogue
Authors: Zi Haur Pang, Yahui Fu, Divesh Lala, Keiko Ochi, Koji Inoue, Tatsuya Kawahara
Categories: cs.CL
Published: 2024-02-20
arXiv: 2402.12770v1
Abstract:
In the realm of human-AI dialogue, the facilitation of empathetic responses is important. Validation is one of the key communication techniques in psychology, which entails recognizing, understanding, and acknowledging others’ emotional states, thoughts, and actions. This study introduces the first framework designed to engender empathetic dialogue with validating responses. Our approach incorporates a tripartite module system: 1) validation timing detection, 2) users’ emotional state identification, and 3) validating response generation. Utilizing Japanese EmpatheticDialogues dataset - a textual-based dialogue dataset consisting of 8 emotional categories from Plutchik’s wheel of emotions - the Task Adaptive Pre-Training (TAPT) BERT-based model outperforms both random baseline and the ChatGPT performance, in term of F1-score, in all modules. Further validation of our model’s efficacy is confirmed in its application to the TUT Emotional Storytelling Corpus (TESC), a speech-based dialogue dataset, by surpassing both random baseline and the ChatGPT. This consistent performance across both textual and speech-based dialogues underscores the effectiveness of our framework in fostering empathetic human-AI communication.
8. You Impress Me: Dialogue Generation via Mutual Persona Perception
Authors: Qian Liu, Yihong Chen, Bei Chen, Jian-Guang Lou, Zixuan Chen, Bin Zhou, Dongmei Zhang
Categories: cs.CL, cs.AI
Published: 2020-04-11
arXiv: 2004.05388v1
Abstract:
Despite the continuing efforts to improve the engagingness and consistency of chit-chat dialogue systems, the majority of current work simply focus on mimicking human-like responses, leaving understudied the aspects of modeling understanding between interlocutors. The research in cognitive science, instead, suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P^2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding. Specifically, P^2 Bot incorporates mutual persona perception to enhance the quality of personalized dialogue generation. Experiments on a large public dataset, Persona-Chat, demonstrate the effectiveness of our approach, with a considerable boost over the state-of-the-art baselines across both automatic metrics and human evaluations.
9. Deep Active Learning for Dialogue Generation
Authors: Nabiha Asghar, Pascal Poupart, Xin Jiang, Hang Li
Categories: cs.CL, cs.AI, cs.NE
Published: 2016-12-12
arXiv: 1612.03929v5
Abstract:
We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. It is trained using a unique combination of offline two-phase supervised learning and online human-in-the-loop active learning. While most existing research proposes offline supervision or hand-crafted reward functions for online reinforcement, we devise a novel interactive learning mechanism based on hamming-diverse beam search for response generation and one-character user-feedback at each step. Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational styles.
10. On the Effectiveness of Offline RL for Dialogue Response Generation
Authors: Paloma Sodhi, Felix Wu, Ethan R. Elenberg, Kilian Q. Weinberger, Ryan McDonald
Categories: cs.CL
Published: 2023-07-23
arXiv: 2307.12425v1
Abstract:
A common training technique for language models is teacher forcing (TF). TF attempts to match human language exactly, even though identical meanings can be expressed in different ways. This motivates use of sequence-level objectives for dialogue response generation. In this paper, we study the efficacy of various offline reinforcement learning (RL) methods to maximize such objectives. We present a comprehensive evaluation across multiple datasets, models, and metrics. Offline RL shows a clear performance improvement over teacher forcing while not inducing training instability or sacrificing practical training budgets.
11. Generative Deep Neural Networks for Dialogue: A Short Review
Authors: Iulian Vlad Serban, Ryan Lowe, Laurent Charlin, Joelle Pineau
Categories: cs.CL, cs.AI, cs.NE
Published: 2016-11-18
arXiv: 1611.06216v1
Abstract:
Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq) models have shown promising results for unstructured tasks, such as word-level dialogue response generation. The hope is that such models will be able to leverage massive amounts of data to learn meaningful natural language representations and response generation strategies, while requiring a minimum amount of domain knowledge and hand-crafting. An important challenge is to develop models that can effectively incorporate dialogue context and generate meaningful and diverse responses. In support of this goal, we review recently proposed models based on generative encoder-decoder neural network architectures, and show that these models have better ability to incorporate long-term dialogue history, to model uncertainty and ambiguity in dialogue, and to generate responses with high-level compositional structure.
12. FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems
Authors: Zihao Wang, Eugene Agichtein, Jinho Choi
Categories: cs.CL, cs.AI
Published: 2023-03-31
arXiv: 2304.00180v1
Abstract:
Response ranking in dialogues plays a crucial role in retrieval-based conversational systems. In a multi-turn dialogue, to capture the gist of a conversation, contextual information serves as essential knowledge to achieve this goal. In this paper, we present a flexible neural framework that can integrate contextual information from multiple channels. Specifically for the current task, our approach is to provide two information channels in parallel, Fusing Conversation history and domain knowledge extracted from Candidate provenance (FCC), where candidate responses are curated, as contextual information to improve the performance of multi-turn dialogue response ranking. The proposed approach can be generalized as a module to incorporate miscellaneous contextual features for other context-oriented tasks. We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks. Our experimental results show that our framework significantly outperforms the previous state-of-the-art models, improving Recall@1 by 7% and MAP by 4%. Furthermore, we conduct ablation studies to evaluate the contributions of each information channel, and of the framework components, to the overall ranking performance, providing additional insights and directions for further improvements.
13. IndoToD: A Multi-Domain Indonesian Benchmark For End-to-End Task-Oriented Dialogue Systems
Authors: Muhammad Dehan Al Kautsar, Rahmah Khoirussyifa’ Nurdini, Samuel Cahyawijaya, Genta Indra Winata, Ayu Purwarianti
Categories: cs.CL, cs.AI
Published: 2023-11-02
arXiv: 2311.00958v1
Abstract:
Task-oriented dialogue (ToD) systems have been mostly created for high-resource languages, such as English and Chinese. However, there is a need to develop ToD systems for other regional or local languages to broaden their ability to comprehend the dialogue contexts in various languages. This paper introduces IndoToD, an end-to-end multi domain ToD benchmark in Indonesian. We extend two English ToD datasets to Indonesian, comprising four different domains by delexicalization to efficiently reduce the size of annotations. To ensure a high-quality data collection, we hire native speakers to manually translate the dialogues. Along with the original English datasets, these new Indonesian datasets serve as an effective benchmark for evaluating Indonesian and English ToD systems as well as exploring the potential benefits of cross-lingual and bilingual transfer learning approaches.
14. Dialogue System of Team NTT-EASE for DRC2023
Authors: Yuki Kubo, Tomoya Yamashita, Masanori Yamada
Categories: cs.HC
Published: 2023-12-21
arXiv: 2312.13734v1
Abstract:
We developed a dialogue system as a team NTT-EASE in the Dialogue Robot Competition 2023 (DRC2023). We introduce a dialogue system (EASE-DRCBot) constructed for DRC2023. EASE-DRCBot incorporates a manually defined dialogue flow. The conditions for system utterances are based on keyword extraction, example-based method, and sentiment analysis. For answering a user’s question, EASE-DRCBot utilizes GPT-3.5 to generate responses. We analyze the results of the preliminary round and explain future works.
15. Comparative Analysis of AutoML and BiLSTM Models for Cyberbullying Detection on Indonesian Instagram Comments
Authors: Raihana Adelia Putri, Aisyah Musfirah, Anggi Puspita Ningrum, Luluk Muthoharoh, Ardika Satria, Martin Clinton Tosima Manullang
Categories: cs.CL
Published: 2026-04-29
arXiv: 2604.26229v1
Abstract:
This study compares machine learning and deep learning approaches for cyberbullying detection in Indonesian-language Instagram comments. Using a balanced dataset of 650 comments labeled as Bullying and Non-Bullying, the study evaluates Naive Bayes, Logistic Regression, and Support Vector Machine with TF-IDF features, as well as BiLSTM and BiLSTM with Bahdanau Attention. A preprocessing pipeline tailored to informal Indonesian text is applied, including slang normalization, stopword removal, and stemming. The results show that Logistic Regression performs best among the machine learning models, while BiLSTM with Attention achieves the strongest overall deep learning performance. The findings highlight the value of domain-specific preprocessing and show that although deep learning captures contextual patterns more effectively, machine learning remains a competitive option for resource-constrained deployments.
16. Enhancing Consistency in Multimodal Dialogue System Using LLM with Dialogue Scenario
Authors: Hiroki Onozeki, Zhiyang Qi, Kazuma Akiyama, Ryutaro Asahara, Takumasa Kaneko, Michimasa Inaba
Categories: cs.CL
Published: 2023-12-20
arXiv: 2312.12808v1
Abstract:
This paper describes our dialogue system submitted to Dialogue Robot Competition 2023. The system’s task is to help a user at a travel agency decide on a plan for visiting two sightseeing spots in Kyoto City that satisfy the user. Our dialogue system is flexible and stable and responds to user requirements by controlling dialogue flow according to dialogue scenarios. We also improved user satisfaction by introducing motion and speech control based on system utterances and user situations. In the preliminary round, our system was ranked fifth in the impression evaluation and sixth in the plan evaluation among all 12 teams.
17. Improving User’s Sense of Participation in Robot-Driven Dialogue
Authors: Makoto Kawamoto, Masaki Shuzo, Eisaku Maeda
Categories: cs.RO
Published: 2022-10-18
arXiv: 2210.09746v1
Abstract:
In task-oriented dialogues with symbiotic robots, the robot usually takes the initiative in dialogue progression and topic selection. In such robot-driven dialogue, the user’s sense of participation in the dialogue is reduced because the degree of freedom in timing and content of speech is limited, and as a result, the user’s familiarity with and trust in the robot as a dialogue partner and the level of dialogue satisfaction decrease. In this study, we constructed a travel agent dialogue system focusing on improving the sense of dialogue participation. At the beginning of the dialogue, the robot tells the user the purpose of the upcoming dialogue and indicates that it is responsible for assisting the user in making decisions. In addition, in situations where users were asked to state their preferences, the robot encourages them to express their intentions with actions, as well as spoken language responses. In addition, we attempted to reduce the sense of discomfort felt toward the android robot by devising a timing control for the robot’s detailed movements and facial expressions.
18. Dialogue system with humanoid robot
Authors: Koki Inoue, Shuichiro Ogake, Hayato Kawamura, Naoki Igo
Categories: cs.RO, q-fin.CP
Published: 2022-10-18
arXiv: 2210.10151v1
Abstract:
Today, as seen in smart speakers, spoken dialogue technology is rapidly advancing to enable human-like interaction. However, current dialogue systems cannot pay attention not only to the content of speech, but also to the way of speaking and eye contact and facial expressions, while watching the facial expressions of the person with whom one is speaking. Therefore, this study participated in a Japanese competition called the “Dialogue Robot Competition” and attempted to develop a dialogue system that includes control of not only the content of speech but also the robot’s facial expressions and gaze in order to realize a humanoid robot that can naturally interact with humans.
19. Benchmarking PyCaret AutoML Against IndoBERT Fine-Tuning for Sentiment Analysis on Indonesian IKN Twitter Data
Authors: Mutia Alfi Mayzaroh, Dwi Fitria Ningsih, Nindi Destriani, Martin C. T. Manullang
Categories: cs.CL
Published: 2026-04-28
arXiv: 2604.25392v1
Abstract:
This paper benchmarks a classical machine learning approach based on PyCaret AutoML against a deep learning approach based on IndoBERT fine-tuning for binary sentiment analysis of Indonesian-language Twitter comments related to Ibu Kota Nusantara (IKN). The dataset contains 1,472 manually labeled samples, consisting of 780 negative and 692 positive comments. In the machine learning setting, Logistic Regression, Naive Bayes, and Support Vector Machine were evaluated using 10-fold cross-validation, with Logistic Regression achieving the best performance among the classical models at 77.57% accuracy and 77.17% F1-score. In the deep learning setting, the indobenchmark/indobert-base-p1 model was fine-tuned for five epochs and achieved 89.59% test accuracy and 89.37% F1-score. The results show that IndoBERT substantially outperforms the machine learning baselines, highlighting the effectiveness of Transformer-based contextual representations for informal Indonesian social media text.
20. SimpleDS: A Simple Deep Reinforcement Learning Dialogue System
Authors: Heriberto Cuayáhuitl
Categories: cs.AI, cs.LG
Published: 2016-01-18
arXiv: 1601.04574v1
Abstract:
This paper presents ‘SimpleDS’, a simple and publicly available dialogue system trained with deep reinforcement learning. In contrast to previous reinforcement learning dialogue systems, this system avoids manual feature engineering by performing action selection directly from raw text of the last system and (noisy) user responses. Our initial results, in the restaurant domain, show that it is indeed possible to induce reasonable dialogue behaviour with an approach that aims for high levels of automation in dialogue control for intelligent interactive agents.
21. Personality-adapted multimodal dialogue system
Authors: Tamotsu Miyama, Shogo Okada
Categories: cs.HC
Published: 2022-10-18
arXiv: 2210.09761v1
Abstract:
This paper describes a personality-adaptive multimodal dialogue system developed for the Dialogue Robot Competition 2022. To realize a dialogue system that adapts the dialogue strategy to individual users, it is necessary to consider the user’s nonverbal information and personality. In this competition, we built a prototype of a user-adaptive dialogue system that estimates user personality during dialogue. Pretrained DNN models are used to estimate user personalities annotated as Big Five scores. This model is embedded in a dialogue system to estimate user personality from face images during the dialogue. We proposed a method for dialogue management that changed the dialogue flow based on the estimated personality characteristics and confirmed that the system works in a real environment in the preliminary round of this competition. Furthermore, we implemented specific modules to enhance the multimodal dialogue experience of the user, including personality assessment, controlling facial expressions and movements of the android, and dialogue management to explain the attractiveness of sightseeing spots. The aim of dialogue based on personality assessment is to reduce the nervousness of users, and it acts as an ice breaker. The android’s facial expressions and movements are necessary for a more natural android conversation. Since the task of this competition was to promote the appeal of sightseeing spots and to recommend an appropriate sightseeing spot, the dialogue process for how to explain the attractiveness of the spot is important. All results of the subjective evaluation by users were better than those of the baseline and other systems developed for this competition. The proposed dialogue system ranked first in both “Impression Rating” and “Effectiveness of Android Recommendations”. According to the total evaluation in the competition, the proposed system was ranked first overall.
22. Dialogue as Discourse: Controlling Global Properties of Scripted Dialogue
Authors: Paul Piwek, Kees van Deemter
Categories: cs.CL, cs.AI
Published: 2003-12-22
arXiv: cs/0312052v1
Abstract:
This paper explains why scripted dialogue shares some crucial properties with discourse. In particular, when scripted dialogues are generated by a Natural Language Generation system, the generator can apply revision strategies that cannot normally be used when the dialogue results from an interaction between autonomous agents (i.e., when the dialogue is not scripted). The paper explains that the relevant revision operators are best applied at the level of a dialogue plan and discusses how the generator may decide when to apply a given revision operator.
23. Proceedings of the Dialogue Robot Competition 2023
Authors: Ryuichiro Higashinaka, Takashi Minato, Hiromitsu Nishizaki, Takayuki Nagai
Categories: cs.RO
Published: 2023-12-22
arXiv: 2312.14430v5
Abstract:
The Dialogic Robot Competition 2023 (DRC2023) is a competition for humanoid robots (android robots that closely resemble humans) to compete in interactive capabilities. This is the third year of the competition. The top four teams from the preliminary competition held in November 2023 will compete in the final competition on Saturday, December 23. The task for the interactive robots is to recommend a tourism plan for a specific region. The robots can employ multimodal behaviors, such as language and gestures, to engage the user in the sightseeing plan they recommend. In the preliminary round, the interactive robots were stationed in a travel agency office, where visitors conversed with them and rated their performance via a questionnaire. In the final round, dialogue researchers and tourism industry professionals interacted with the robots and evaluated their performance. This event allows visitors to gain insights into the types of dialogue services that future dialogue robots should offer. The proceedings include papers on dialogue systems developed by the 12 teams participating in DRC2023, as well as an overview of the papers provided by all the teams.
24. Meta-control of Dialogue Systems Using Large Language Models
Authors: Kotaro Shukuri, Ryoma Ishigaki, Jundai Suzuki, Tsubasa Naganuma, Takuma Fujimoto, Daisuke Kawakubo, Masaki Shuzo, Eisaku Maeda
Categories: cs.RO
Published: 2023-12-21
arXiv: 2312.13715v1
Abstract:
Utilizing Large Language Models (LLMs) facilitates the creation of flexible and natural dialogues, a task that has been challenging with traditional rule-based dialogue systems. However, LLMs also have the potential to produce unexpected responses, which may not align with the intentions of dialogue system designers. To address this issue, this paper introduces a meta-control method that employs LLMs to develop more stable and adaptable dialogue systems. The method includes dialogue flow control to ensure that utterances conform to predefined scenarios and turn-taking control to foster natural dialogues. Furthermore, we have implemented a dialogue system that utilizes this meta-control strategy and verified that the dialogue system utilizing meta-control operates as intended.
25. Single- vs. Dual-Prompt Dialogue Generation with LLMs for Job Interviews in Human Resources
Authors: Joachim De Baer, A. Seza Doğruöz, Thomas Demeester, Chris Develder
Categories: cs.CL
Published: 2025-02-25
arXiv: 2502.18650v2
Abstract:
Optimizing language models for use in conversational agents requires large quantities of example dialogues. Increasingly, these dialogues are synthetically generated by using powerful large language models (LLMs), especially in domains where obtaining authentic human data is challenging. One such domain is human resources (HR). In this context, we compare two LLM-based dialogue generation methods for producing HR job interviews, and assess which method generates higher-quality dialogues, i.e., those more difficult to distinguish from genuine human discourse. The first method uses a single prompt to generate the complete interview dialogue. The second method uses two agents that converse with each other. To evaluate dialogue quality under each method, we ask a judge LLM to determine whether AI was used for interview generation, using pairwise interview comparisons. We empirically find that, at the expense of a sixfold increase in token count, interviews generated with the dual-prompt method achieve a win rate 2 to 10 times higher than those generated with the single-prompt method. This difference remains consistent regardless of whether GPT-4o or Llama 3.3 70B is used for either interview generation or quality judging.
26. Overview of Dialogue Robot Competition 2022
Authors: Takashi Minato, Ryuichiro Higashinaka, Kurima Sakai, Tomo Funayama, Hiromitsu Nishizaki, Takayuki Nagai
Categories: cs.RO, cs.HC
Published: 2022-10-23
arXiv: 2210.12863v1
Abstract:
Although many competitions have been held on dialogue systems in the past, no competition has been organized specifically for dialogue with humanoid robots. As the first such attempt in the world, we held a dialogue robot competition in 2020 to compare the performances of interactive robots using an android that closely resembles a human. Dialogue Robot Competition 2022 (DRC2022) was the second competition, held in August 2022. The task and regulations followed those of the first competition, while the evaluation method was improved and the event was internationalized. The competition has two rounds, a preliminary round and the final round. In the preliminary round, twelve participating teams competed in performance of a dialogue robot in the manner of a field experiment, and then three of those teams were selected as finalists. The final round will be held on October 25, 2022, in the Robot Competition session of IROS2022. This paper provides an overview of the task settings and evaluation method of DRC2022 and the results of the preliminary round.
27. Dialogue Response Prefetching Based on Semantic Similarity and Prediction Confidence of Language Model
Authors: Kiyotada Mori, Seiya Kawano, Angel Fernando Garcia Contreras, Koichiro Yoshino
Categories: cs.CL
Published: 2025-08-06
arXiv: 2508.04403v2
Abstract:
Prefetching of dialogue responses has been investigated to reduce user-perceived latency (UPL), which refers to the user’s waiting time before receiving the system’s response, in spoken dialogue systems. To reduce the UPL, it is necessary to predict complete user utterances before the end of the user’s speech, typically by language models, to prepare prefetched dialogue responses. In this study, we proposed a prediction confidence model (PCM) that determines whether prefetching is possible or not by estimating the semantic similarity between the predicted complete user utterance and the complete user utterance. We evaluated our PCM based on the differences between the predicted complete user utterance and the complete user utterance.
28. $C^3$: Compositional Counterfactual Contrastive Learning for Video-grounded Dialogues
Authors: Hung Le, Nancy F. Chen, Steven C. H. Hoi
Categories: cs.LG, cs.CL, cs.CV
Published: 2021-06-16
arXiv: 2106.08914v2
Abstract:
Video-grounded dialogue systems aim to integrate video understanding and dialogue understanding to generate responses that are relevant to both the dialogue and video context. Most existing approaches employ deep learning models and have achieved remarkable performance, given the relatively small datasets available. However, the results are partly accomplished by exploiting biases in the datasets rather than developing multimodal reasoning, resulting in limited generalization. In this paper, we propose a novel approach of Compositional Counterfactual Contrastive Learning ($C^3$) to develop contrastive training between factual and counterfactual samples in video-grounded dialogues. Specifically, we design factual/counterfactual sampling based on the temporal steps in videos and tokens in dialogues and propose contrastive loss functions that exploit object-level or action-level variance. Different from prior approaches, we focus on contrastive hidden state representations among compositional output tokens to optimize the representation space in a generation setting. We achieved promising performance gains on the Audio-Visual Scene-Aware Dialogues (AVSD) benchmark and showed the benefits of our approach in grounding video and dialogue context.
29. Benchmarking LightGBM and BiLSTM for Sentiment Analysis on Indonesian E-Commerce Reviews
Authors: Lidia Natasyah Marpaung, Vania Claresta, Iqfina Haula Halika, Luluk Muthoharoh, Ardika Satria, Martin Clinton Tosima Manullang
Categories: cs.CL
Published: 2026-05-02
arXiv: 2605.01322v1
Abstract:
This study presents a comparative analysis between two primary approaches in Natural Language Processing (NLP): Machine Learning (ML) utilizing the PyCaret AutoML framework, and Deep Learning (DL). The evaluation is conducted on a sentiment analysis task using an Indonesian e-commerce review dataset sourced from Hugging Face. The dataset, consisting of 15,000 samples, is partitioned into training, validation, and testing sets. The ML experiments compare LightGBM, Logistic Regression, and Support Vector Machine (SVM) algorithms, whereas the DL experiment implements a Bidirectional Long Short-Term Memory (BiLSTM) architecture. The experimental results demonstrate that the BiLSTM model outperforms all ML models, achieving an accuracy of 98.87% and an F1-Score of 98.87%. Meanwhile, LightGBM emerges as the best-performing ML model with an accuracy of 98.23% in a highly efficient training time. This research proves that the BiLSTM architecture is highly capable of capturing the sequential context of Indonesian review texts, making it the superior model for this specific classification task.
30. Proceedings of the Dialogue Robot Competition 2022
Authors: Ryuichiro Higashinaka, Takashi Minato, Hiromitsu Nishizaki, Takayuki Nagai
Categories: cs.RO
Published: 2022-10-21
arXiv: 2210.12034v4
Abstract:
The proceedings contain papers on the dialogue systems developed by the twelve teams participating in DRC2022, as well as an overview paper summarizing the competition.
31. Team OS’s System for Dialogue Robot Competition 2022
Authors: Yuki Kubo, Ryo Yanagimoto, Hayato Futase, Mikio Nakano, Zhaojie Luo, Kazunori Komatani
Categories: cs.HC
Published: 2022-10-18
arXiv: 2210.09928v1
Abstract:
This paper describes our dialogue robot system, OSbot, developed for Dialogue Robot Competition 2022. The dialogue flow is based on state transitions described manually and the transition conditions use the results of keyword extraction and sentiment analysis. The transitions can be easily viewed and edited by managing them on a spreadsheet. The keyword extraction is based on named entity extraction and our predefined keyword set. The sentiment analysis is text-based and uses SVM, which was trained with the multimodal dialogue corpus Hazumi. We quickly checked and edited a dialogue flow by using a logging function. In the competition’s preliminary round, our system ended up in third place.
32. A Summarized History-based Dialogue System for Amnesia-Free Prompt Updates
Authors: Hyejin Hong, Hibiki Kawano, Takuto Maekawa, Naoki Yoshimaru, Takamasa Iio, Kenji Hatano
Categories: cs.RO
Published: 2023-12-21
arXiv: 2312.13891v1
Abstract:
In today’s society, information overload presents challenges in providing optimal recommendations. Consequently, the importance of dialogue systems that can discern and provide the necessary information through dialogue is increasingly recognized. However, some concerns existing dialogue systems rely on pre-trained models and need help to cope with real-time or insufficient information. To address these concerns, models that allow the addition of missing information to dialogue robots are being proposed. Yet, maintaining the integrity of previous conversation history while integrating new data remains a formidable challenge. This paper presents a novel system for dialogue robots designed to remember user-specific characteristics by retaining past conversation history even as new information is added.
33. Android dialogue system for customer service using prompt-based topic control and compliments generation
Authors: Miyama Tamotsu, Okada Shogo
Categories: cs.HC
Published: 2023-12-20
arXiv: 2312.12924v1
Abstract:
This paper describes a dialogue system developed for the Dialogue Robot Competition 2023 that achieves topic control for trip planning by inserting text into prompts using the ChatGPT-API. We built a system that is capable of generating compliments for the user based on recognition of the user’s appearance and creating travel plans by extracting the knowledge about the user’s preference from the history of the user’s utterances. Complements and planning based on preference are the elements required to maintain the quality of customer service. A preliminary round was held at a travel agency’s actual store, where real customers experienced and evaluated the system. This system was evaluated first in the preliminary round and participated in the final round. The results of the preliminary round showed the effectiveness of the proposed system.
34. Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch
Authors: Donglin Di, Weinan Zhang, Yue Zhang, Fanglin Wang
Categories: cs.CL
Published: 2024-10-24
arXiv: 2410.18430v1
Abstract:
Making use of off-the-shelf resources of resource-rich languages to transfer knowledge for low-resource languages raises much attention recently. The requirements of enabling the model to reach the reliable performance lack well guided, such as the scale of required annotated data or the effective framework. To investigate the first question, we empirically investigate the cost-effectiveness of several methods to train the intent classification and slot-filling models for Indonesia (ID) from scratch by utilizing the English data. Confronting the second challenge, we propose a Bi-Confidence-Frequency Cross-Lingual transfer framework (BiCF), composed by
BiCF Mixing'',Latent Space Refinement’’ and ``Joint Decoder’’, respectively, to tackle the obstacle of lacking low-resource language dialogue data. Extensive experiments demonstrate our framework performs reliably and cost-efficiently on different scales of manually annotated Indonesian data. We release a large-scale fine-labeled dialogue dataset (ID-WOZ) and ID-BERT of Indonesian for further research.
35. Towards Automated Generation of Scripted Dialogue: Some Time-Honoured Strategies
Authors: Paul Piwek, Kees van Deemter
Categories: cs.CL, cs.AI
Published: 2003-12-22
arXiv: cs/0312051v1
Abstract:
The main aim of this paper is to introduce automated generation of scripted dialogue as a worthwhile topic of investigation. In particular the fact that scripted dialogue involves two layers of communication, i.e., uni-directional communication between the author and the audience of a scripted dialogue and bi-directional pretended communication between the characters featuring in the dialogue, is argued to raise some interesting issues. Our hope is that the combined study of the two layers will forge links between research in text generation and dialogue processing. The paper presents a first attempt at creating such links by studying three types of strategies for the automated generation of scripted dialogue. The strategies are derived from examples of human-authored and naturally occurring dialogue.
36. ditlab system for Dialogue Robot Competition 2022
Authors: Yuuki Tachioka
Categories: cs.RO, cs.HC
Published: 2022-10-13
arXiv: 2210.06646v1
Abstract:
We developed a dialogue system for Dialogue Robot Competition 2022. Our system is composed of three parts. First part investigates participants’ demographic information by rule-based interview. Second part recommends a point of interest (POI) based on the collected demographic information. Third part answers participants’ question based on the combination of rule-based answering and deep-learning-based answering with nearby POI search.
37. StagePilot: Stage-Level Planning for Long-Horizon Dialogue Simulation in Cybergrooming
Authors: Heajun An, Qi Zhang, Minqian Liu, Xinyi Zhang, Sang Won Lee, Lifu Huang, Pamela J. Wisniewski, Jin-Hee Cho
Categories: cs.LG, cs.CL
Published: 2026-02-04
arXiv: 2602.05060v2
Abstract:
Cybergrooming is an evolving threat to youth, requiring proactive educational interventions. We address this by modeling dialogue progression as a structured planning problem over stage-wise interactions. We propose StagePilot, a dialogue framework that separates stage-level planning from response generation, in which the model selects the next stage under constrained transitions and generates responses conditioned on it, enabling coherent and realistic progression. Reinforcement learning is used to learn stage-level policies from offline data, optimizing for both emotional alignment and goal-consistent progression. Our empirical experiments show that StagePilot generates more structured, coherent dialogue trajectories and reduces conversational stagnation compared to baselines; notably, the IQL+AWAC variant reaches the final stage more often while maintaining over 70% positive or neutral responses, yielding a 43% relative improvement.
38. Towards End-to-End Learning for Efficient Dialogue Agent by Modeling Looking-ahead Ability
Authors: Zhuoxuan Jiang, Xian-Ling Mao, Ziming Huang, Jie Ma, Shaochun Li
Categories: cs.CL
Published: 2019-08-15
arXiv: 1908.05408v1
Abstract:
Learning an efficient manager of dialogue agent from data with little manual intervention is important, especially for goal-oriented dialogues. However, existing methods either take too many manual efforts (e.g. reinforcement learning methods) or cannot guarantee the dialogue efficiency (e.g. sequence-to-sequence methods). In this paper, we address this problem by proposing a novel end-to-end learning model to train a dialogue agent that can look ahead for several future turns and generate an optimal response to make the dialogue efficient. Our method is data-driven and does not require too much manual work for intervention during system design. We evaluate our method on two datasets of different scenarios and the experimental results demonstrate the efficiency of our model.
39. AsyncMLD: Asynchronous Multi-LLM Framework for Dialogue Recommendation System
Authors: Naoki Yoshimaru, Motoharu Okuma, Takamasa Iio, Kenji Hatano
Categories: cs.HC, cs.AI, cs.RO
Published: 2023-12-21
arXiv: 2312.13925v1
Abstract:
We have reached a practical and realistic phase in human-support dialogue agents by developing a large language model (LLM). However, when requiring expert knowledge or anticipating the utterance content using the massive size of the dialogue database, we still need help with the utterance content’s effectiveness and the efficiency of its output speed, even if using LLM. Therefore, we propose a framework that uses LLM asynchronously in the part of the system that returns an appropriate response and in the part that understands the user’s intention and searches the database. In particular, noting that it takes time for the robot to speak, threading related to database searches is performed while the robot is speaking.
40. Improving Dialogue Breakdown Detection with Semi-Supervised Learning
Authors: Nathan Ng, Marzyeh Ghassemi, Narendran Thangarajan, Jiacheng Pan, Qi Guo
Categories: cs.CL, cs.LG
Published: 2020-10-30
arXiv: 2011.00136v2
Abstract:
Building user trust in dialogue agents requires smooth and consistent dialogue exchanges. However, agents can easily lose conversational context and generate irrelevant utterances. These situations are called dialogue breakdown, where agent utterances prevent users from continuing the conversation. Building systems to detect dialogue breakdown allows agents to recover appropriately or avoid breakdown entirely. In this paper we investigate the use of semi-supervised learning methods to improve dialogue breakdown detection, including continued pre-training on the Reddit dataset and a manifold-based data augmentation method. We demonstrate the effectiveness of these methods on the Dialogue Breakdown Detection Challenge (DBDC) English shared task. Our submissions to the 2020 DBDC5 shared task place first, beating baselines and other submissions by over 12% accuracy. In ablations on DBDC4 data from 2019, our semi-supervised learning methods improve the performance of a baseline BERT model by 2% accuracy. These methods are applicable generally to any dialogue task and provide a simple way to improve model performance.
41. Multimodal Dialogue Response Generation
Authors: Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang
Categories: cs.CL, cs.AI, cs.CV, cs.LG, cs.MM
Published: 2021-10-16
arXiv: 2110.08515v2
Abstract:
Responsing with image has been recognized as an important capability for an intelligent conversational agent. Yet existing works only focus on exploring the multimodal dialogue models which depend on retrieval-based methods, but neglecting generation methods. To fill in the gaps, we first present a multimodal dialogue generation model, which takes the dialogue history as input, then generates a textual sequence or an image as response. Learning such a model often requires multimodal dialogues containing both texts and images which are difficult to obtain. Motivated by the challenge in practice, we consider multimodal dialogue generation under a natural assumption that only limited training examples are available. In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model. By this means, the major part of the model can be learned from a large number of text-only dialogues and text-image pairs respectively, then the whole parameters can be well fitted using the limited training examples. Extensive experiments demonstrate our method achieves state-of-the-art results in both automatic and human evaluation, and can generate informative text and high-resolution image responses.
42. Report from Workshop on Dialogue alongside Artificial Intelligence
Authors: Thomas J McKenna, Ingvill Rasmussen, Sten Ludvigsen, Avivit Arvatz, Christa Asterhan, Gaowei Chen, Julie Cohen, Michele Flammia, Dongkeun Han, Emma Hayward, Heather Hill, Yifat Kolikant, Helen Lehndorf, Kexin Li, Lindsay Clare Matsumura, Henrik Tjønn, Pengjin Wang, Rupert Wegerif
Categories: cs.CY, cs.AI
Published: 2025-11-06
arXiv: 2511.05625v2
Abstract:
Educational dialogue – the collaborative exchange of ideas through talk – is widely recognized as a catalyst for deeper learning and critical thinking in and across contexts. At the same time, artificial intelligence (AI) has rapidly emerged as a powerful force in education, with the potential to address major challenges, personalize learning, and innovate teaching practices. However, these advances come with significant risks: rapid AI development can undermine human agency, exacerbate inequities, and outpace our capacity to guide its use with sound policy. Human learning presupposes cognitive efforts and social interaction (dialogues). In response to this evolving landscape, an international workshop titled “Educational Dialogue: Moving Thinking Forward” convened 19 leading researchers from 11 countries in Cambridge (September 1-3, 2025) to examine the intersection of AI and educational dialogue. This AI-focused strand of the workshop centered on three critical questions: (1) When is AI truly useful in education, and when might it merely replace human effort at the expense of learning? (2) Under what conditions can AI use lead to better dialogic teaching and learning? (3) Does the AI-human partnership risk outpacing and displacing human educational work, and what are the implications? These questions framed two days of presentations and structured dialogue among participants.
43. Team Flow at DRC2022: Pipeline System for Travel Destination Recommendation Task in Spoken Dialogue
Authors: Ryu Hirai, Atsumoto Ohashi, Ao Guo, Hideki Shiroma, Xulin Zhou, Yukihiko Tone, Shinya Iizuka, Ryuichiro Higashinaka
Categories: cs.CL, cs.AI, cs.RO
Published: 2022-10-18
arXiv: 2210.09518v1
Abstract:
To improve the interactive capabilities of a dialogue system, e.g., to adapt to different customers, the Dialogue Robot Competition (DRC2022) was held. As one of the teams, we built a dialogue system with a pipeline structure containing four modules. The natural language understanding (NLU) and natural language generation (NLG) modules were GPT-2 based models, and the dialogue state tracking (DST) and policy modules were designed on the basis of hand-crafted rules. After the preliminary round of the competition, we found that the low variation in training examples for the NLU and failed recommendation due to the policy used were probably the main reasons for the limited performance of the system.
44. A Methodology for Identifying Evaluation Items for Practical Dialogue Systems Based on Business-Dialogue System Alignment Models
Authors: Mikio Nakano, Hironori Takeuchi, Kazunori Komatani
Categories: cs.HC, cs.CL, cs.SE
Published: 2026-01-10
arXiv: 2602.15835v1
Abstract:
This paper proposes a methodology for identifying evaluation items for practical dialogue systems. Traditionally, user satisfaction and user experiences have been the primary metrics for evaluating dialogue systems. However, there are various other evaluation items to consider when developing and operating practical dialogue systems, and such evaluation items are expected to lead to new research topics. So far, there has been no methodology for identifying these evaluation items. We propose identifying evaluation items based on business-dialogue system alignment models, which are applications of business-IT alignment models used in the development and operation of practical IT systems. We also present a generic model that facilitates the construction of a business-dialogue system alignment model for each dialogue system.
45. Hybrid TF–IDF Logistic Regression and MLP Neural Baseline for Indonesian Three-Class Sentiment Analysis on Social Media Text
Authors: Allya Nurul Islami Pasha, Eka Fidiya Putri, Luluk Muthoharoh, Ardika Satria, Martin C. T. Manullang
Categories: cs.CL
Published: 2026-05-08
arXiv: 2605.07793v1
Abstract:
This paper presents a compact three-class sentiment analysis study for Indonesian social media text. The task is formulated with positive, negative, and neutral outputs derived from a fine-grained emotion dataset. The proposed practical baseline combines TF–IDF text features, three lightweight numeric metadata features, and a balanced multinomial Logistic Regression classifier. For comparison, the study also includes a neural baseline using a two-layer multilayer perceptron (MLP) over the same hybrid feature representation. The dataset originally contains 732 rows and 191 fine-grained emotion labels; after cleaning, deduplication, and label remapping, 707 samples remain with an imbalanced distribution of 459 positive, 188 negative, and 60 neutral instances. Experimental results show that the Logistic Regression deployment model reaches 0.8028 accuracy, 0.8003 weighted F1, and 0.7276 macro F1, while project documentation reports a higher-accuracy but non-production MLP baseline. These findings indicate that careful preprocessing, interpretable feature engineering, and class balancing remain competitive for small Indonesian sentiment datasets, whereas the neural baseline is better treated as a comparative experiment than as the default deployment model.
46. EM Pre-training for Multi-party Dialogue Response Generation
Authors: Yiyang Li, Hai Zhao
Categories: cs.CL
Published: 2023-05-21
arXiv: 2305.12412v1
Abstract:
Dialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great gap for multi-party dialogues at the same time. Different from two-party dialogues where each response is a direct reply to its previous utterance, the addressee of a response utterance should be specified before it is generated in the multi-party scenario. Thanks to the huge amount of two-party conversational data, various pre-trained language models for two-party dialogue response generation have been proposed. However, due to the lack of annotated addressee labels in multi-party dialogue datasets, it is hard to use them to pre-train a response generation model for multi-party dialogues. To tackle this obstacle, we propose an Expectation-Maximization (EM) approach that iteratively performs the expectation steps to generate addressee labels, and the maximization steps to optimize a response generation model. Theoretical analyses and extensive experiments have justified the feasibility and effectiveness of our proposed method.
47. Multi-Domain Dialogue Acts and Response Co-Generation
Authors: Kai Wang, Junfeng Tian, Rui Wang, Xiaojun Quan, Jianxing Yu
Categories: cs.CL, cs.AI
Published: 2020-04-26
arXiv: 2004.12363v1
Abstract:
Generating fluent and informative responses is of critical importance for task-oriented dialogue systems. Existing pipeline approaches generally predict multiple dialogue acts first and use them to assist response generation. There are at least two shortcomings with such approaches. First, the inherent structures of multi-domain dialogue acts are neglected. Second, the semantic associations between acts and responses are not taken into account for response generation. To address these issues, we propose a neural co-generation model that generates dialogue acts and responses concurrently. Unlike those pipeline approaches, our act generation module preserves the semantic structures of multi-domain dialogue acts and our response generation module dynamically attends to different acts as needed. We train the two modules jointly using an uncertainty loss to adjust their task weights adaptively. Extensive experiments are conducted on the large-scale MultiWOZ dataset and the results show that our model achieves very favorable improvement over several state-of-the-art models in both automatic and human evaluations.
48. Evaluate On-the-job Learning Dialogue Systems and a Case Study for Natural Language Understanding
Authors: Mathilde Veron, Sophie Rosset, Olivier Galibert, Guillaume Bernard
Categories: cs.CL
Published: 2021-02-26
arXiv: 2102.13589v1
Abstract:
On-the-job learning consists in continuously learning while being used in production, in an open environment, meaning that the system has to deal on its own with situations and elements never seen before. The kind of systems that seem to be especially adapted to on-the-job learning are dialogue systems, since they can take advantage of their interactions with users to collect feedback to adapt and improve their components over time. Some dialogue systems performing on-the-job learning have been built and evaluated but no general methodology has yet been defined. Thus in this paper, we propose a first general methodology for evaluating on-the-job learning dialogue systems. We also describe a task-oriented dialogue system which improves on-the-job its natural language component through its user interactions. We finally evaluate our system with the described methodology.
49. Spoken Dialogue System Based on Attribute Vector for Travel Agent Robot
Authors: Motoyuki Suzuki, Shintaro Sodeya, Taichi Nakamura
Categories: cs.HC
Published: 2022-10-17
arXiv: 2210.08703v1
Abstract:
In this study, we develop a dialogue system for a dialogue robot competition. In the system, the characteristics of sightseeing spots are expressed as “attribute vectors” in advance, and the user is questioned on the different attributes of the two candidate spots. Consequently, the system can make recommendations based on user intentions. A dialogue experiment is conducted during a preliminary round of competition. The overall satisfaction score obtained is 40.1 out of 63 points, which is a reasonable result. Analysis of the relationship between the system behavior and satisfaction scores reveals that satisfaction increases when the system correctly understands the user intention and responds appropriately. However, a negative correlation is observed between the number of user utterances and the satisfaction score. This implies that inappropriate responses reduce the usefulness of the system as a consultation partner.
50. The Complex Negotiation Dialogue Game
Authors: Romain Laroche
Categories: cs.AI, cs.CL
Published: 2017-07-05
arXiv: 1707.01450v1
Abstract:
This position paper formalises an abstract model for complex negotiation dialogue. This model is to be used for the benchmark of optimisation algorithms ranging from Reinforcement Learning to Stochastic Games, through Transfer Learning, One-Shot Learning or others.