Recipes for Building an Open-Domain Chatbot

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515

Scaling Laws for Neural Language Models

Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · doi:10.48550/arxiv.2001.08361 · Cited by 9,550 · cited 1×

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019) · arXiv · doi:10.48550/arxiv.1910.13461 · Cited by 8,816

Longformer: The Long-Document Transformer

Beltagy, I., Peters, M. E., & Cohan, A. (2020) · arXiv · doi:10.48550/arxiv.2004.05150 · Cited by 2,281

Transformer-XL: Attentive Language Models Beyond a Fixed-Length ContextPreprint

Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V., & Salakhutdinov, R. (2019) · arXiv (Cornell University) · doi:10.4230/lipics.ecrts.2023.7 · Cited by 1,476

Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation

Zhang, Z., Cheng, J., Sun, H., Deng, J., Mi, F., Wang, Y., Shang, L., & Huang, M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.270 · Cited by 9

On the Effectiveness of Automated Metrics for Text Generation Systems

von Däniken, P., Deriu, J., Tuggener, D., & Cieliebak, M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.108 · Cited by 3

Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games

Towle, B., & Zhou, K. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.364 · Cited by 3

Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent

Chi, E. A., Paranjape, A., See, A., Chiam, C., Chang, T., Kenealy, K., Lim, S. K., Hardy, A., Rastogi, C., Li, H., et al. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.37 · Cited by 6

Modelling direct messaging networks with multiple recipients for cyber deception

Moore, K., Christopher, C. J., Liebowitz, D., Nepal‬, ‪., & Selvey, R. (2022) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp53844.2022.00009 · Cited by 6

Controlling the Focus of Pretrained Language Generation Models

Ji, J., Kim, Y., Glass, J. R., & He, T. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.260 · Cited by 4

Towards a Progression-Aware Autonomous Dialogue Agent

Sanders, A., Strzalkowski, T., Si, M., Chang, A., Dey, D., Braasch, J., & Wang, D. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.87 · Cited by 3

Zero-shot Generalization of Multimodal Dialogue Agents

Tavares, D. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548759 · Cited by 3

SuperDialseg: A Large-scale Dataset for Supervised Dialogue Segmentation

Jiang, J., Dong, C., Kurohashi, S., & Aizawa, A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.249 · Cited by 6

Endowing Empathetic Dialogue Systems with Personas.Preprint

Zhong, P., Sun, Y., Liu, Y., Zhang, C., Wang, H., Nie, Z., & Miao, C. (2020) · arXiv (Cornell University) · Cited by 5

Pretraining the Noisy Channel Model for Task-Oriented DialoguePreprint

Liu, Q., Yu, L., Rimell, L., & Blunsom, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.10518 · Cited by 3

Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-rankingPreprint

Soni, M., Cowan, B. R., & Wade, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05090 · Cited by 3

NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative EnvironmentPreprint

Kiseleva, J., Li, Z., Aliannejadi, M., Mohanty, S., ter Hoeve, M., Burtsev, M., Skrynnik, A., Zholus, A., Panov, A. I., Srinet, K., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06536 · Cited by 5

An Approach to Inference-Driven Dialogue Management within a Social ChatbotPreprint

Finch, S. E., Finch, J. D., Huryn, D., Hutsell, W., Huang, X., He, H. W., & Choi, J. D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.00570 · Cited by 3

IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant

Ji, T., Yin, X., Cheng, P., Zhou, L., Liu, S., Bao, W., & Lyu, C. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192315493 · Cited by 2

Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions

Salcedo, J., Solano, J., García, H. F., Zarruk, D., & Correa-Bahnsen, A. (2022) · LatinX in AI at Neural Information Processing Systems Conference (LXAI) · doi:10.52591/lxai202211287 · Cited by 6

Temporal Extraction and Retrieval

Alonso, O., & Spaniol, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674137 · Cited by 2

Neural Information Retrieval

Tonellotto, N. (2024) · ACM eBooks · doi:10.1145/3674127.3674130 · Cited by 3

Enhancing Language Models Through Prompt Engineering - A Survey

Padmaja, C. V. R., & Lakshminarayana, S. (2024) · IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT) · doi:10.1109/icissgt58904.2024.00033 · Cited by 2

Dialogue response coherency evaluation with feature sensitive negative sample using multi list-wise ranking loss

Hwang, Y., Kang, D., & Bak, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110609 · Cited by 2

Generating, retrieving persona and generating responses for long-term open-domain dialogue

Cha, D., Lee, D., & Kim, J. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2979 · Cited by 2

Generative Artificial Intelligence and Large Language Models: A Systematic Review of Architectures, Applications, and Future Directions

Ciubotaru, B.-I. (2025) · Computer Science in Cars Symposium · doi:10.1109/cscs66924.2025.00063 · Cited by 1

Towards Efficient LLM Inference via Collective and Adaptive Speculative Decoding

Wang, S., Yang, H., Wang, X., Liu, T., Wang, P., Xu, Y., Liang, X., Ma, K., Feng, T., You, X., et al. (2025) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3712285.3759834 · Cited by 2

An Intelligent Multilingual Legal Chatbot for Algerian Law Based on RAG and LLMs

Lahiani, N., & MERZOUG, A. (2025) · International Conference on Intelligent Computer Systems, Data Science and Applications (IC2SDA) · doi:10.1109/ic2sda68097.2025.11331471 · Cited by 3

Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence

Mitchell, E. G. (2021) · doi:10.7916/d8-drwc-b875 · Cited by 2

Classification of Utterances that Lead to Dialogue Breakdowns in Chat-oriented Dialogue Systems

Higashinaka, R., Araki, M., Tsukahara, H., & Mizukami, M. (2022) · Journal of Natural Language Processing · doi:10.5715/jnlp.29.443 · Cited by 2

Grounding in social media: An approach to building a chit-chat dialogue model

Choudhary, R., & Kawahara, D. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/2022.naacl-srw.2 · Cited by 3

Chatbot Interaksi Rumah Sakit menggunakan FFNN

Chandra, M., Pratama, R., Pradana, F. A., & Bonita, A. (2022) · Indonesian Journal of Data and Science · doi:10.56705/ijodas.v3i1.36 · Cited by 4

Mitigating Negative Style Transfer in Hybrid Dialogue System

Li, S., Cheng, Q., Li, L., & Qiu, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26539 · Cited by 2

Towards Zero-Shot Persona Dialogue Generation with In-Context Learning

Xu, X., Lei, Z., Wu, W., Niu, Z.-Y., Wu, H. R., & Wang, H. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.90 · Cited by 2

Adaptive Kevin: A Multipurpose AI Assistant for Higher Education

Gonzalez-Bonorino, A., & Lauría, E. J. M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-40501-3_7 · Cited by 2

A Friend in Need Is a Friend Indeed: Investigating the Quality of Training Data from Peers for Auto-generating Empathetic Textual Responses to Non-Sensitive Posts in a Cohort of College Students

Sharma, R. S., Mirzakhalov, J., Bharti, P., Goyal, R., Schmidt, T., & Chellappan, S. (2023) · ACM Journal on Computing and Sustainable Societies · doi:10.1145/3616382 · Cited by 3

Multi-modal Multi-emotion Emotional Support Conversation

Liu, G., Dong, X. M., Wang, M., Yu, J., Gan, M., Liu, W., & Yin, J. J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46661-8_20 · Cited by 2

Prompting, Retrieval, Training: An exploration of different approaches for task-oriented dialogue generation

Raposo, G., Coheur, L., & Martins, B. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.37 · Cited by 4

Multi-Source Multi-Type Knowledge Exploration and Exploitation for Dialogue Generation

Ni, X., Dai, H., Ren, Z., & Li, P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.771 · Cited by 2

A Framework for Vision-Language Warm-up Tasks in Multimodal Dialogue Models

Lee, J., Park, S., Park, S., Kim, H., & Kim, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.167 · Cited by 2

Persona-Identified Chatbot through Small-Scale Modeling and Data Transformation

Keum, B., Sun, J., Lee, W., Park, S., & Kim, H. (2024) · Electronics · doi:10.3390/electronics13081409 · Cited by 2

Empathy in AI: Developing a Sentiment-Sensitive Chatbot through Advanced Natural Language Processing

(2024) · International Journal of Advanced Trends in Computer Science and Engineering · doi:10.30534/ijatcse/2024/071332024 · Cited by 2

Toward Immersive Computational Storytelling: Card-Framework for Enhanced Persona-Driven Dialogues

Liao, B., & Vargas, D. V. (2024) · IEEE Transactions on Games · doi:10.1109/tg.2024.3466898 · Cited by 2

Professional Search

Verberne, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674141 · Cited by 5

Evaluation of IR Systems

Ferro, N., & Maistro, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674132 · Cited by 3

Privacy in Information Retrieval

Soboroff, I. M. (2024) · ACM eBooks · doi:10.1145/3674127.3674139 · Cited by 2

Internet Wizard for Enhancing Open Domain Question Answering Chatbot Knowledge-Base in Information Seeking

Mzwri, K., & Turcsányi-Szabó, M. (2025) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3112-5.ch007 · Cited by 1

DialFill: Utilizing Dialogue Filling to Integrate Retrieved Knowledge in Responses

Xue, Q., Takiguchi, T., & Ariki, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3555650 · Cited by 2

What Did You Refer to? Evaluating Co-References in Dialogue

Zhang, W., Zhang, Y., Tang, H., Zhao, Z., Zhu, C., & Liu, T. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.450 · Cited by 2

Should we tweet this? Generative response modeling for predicting reception of public health messaging on Twitter

Sanders, A., Ray-Majumder, D., Erickson, J., & Bennett, K. P. (2022) · ACM Web Science Conference (WebSci) · doi:10.1145/3501247.3531574 · Cited by 2

Establishing the Informational Requirements for Modelling Open Domain Dialogue and Prototyping a Retrieval Open Domain Dialogue System

Meier, T., & Pimenidis, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88081-1_49 · Cited by 2

Analysis of Empathic Dialogue in Actual Doctor-Patient Calls and Implications for Design of Embodied Conversational Agents

Salman, S., Richards, D., & Caldwell, P. H. Y. (2021) · Italian Journal of Computational Linguistics · doi:10.4000/ijcol.862 · Cited by 3

Explicit Use of Topicality in Dialogue Response Generation

Yoshikoshi, T., Atarashi, H., Kodama, T., & Kurohashi, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/2022.naacl-srw.28 · Cited by 2

Automatic Detection of Inconsistencies in Open-Domain Chatbots

Prats, J. M., Estecha-Garitagoitia, M., Rodríguez-Cantelar, M., & D’Haro, L. F. (2022) · IberSPEECH · doi:10.21437/iberspeech.2022-24 · Cited by 2

HumanoidBot: Full-Body Humanoid Chitchat System

Seppelfelt, G. D. C., Asaka, T., Nagai, T., & Yukizaki, S. (2022) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids53995.2022.10000209 · Cited by 2

Contrastive Learning with Dialogue Attributes for Neural Dialogue Generation

Tan, J., Cai, H., Chen, H., Cheng, H., Meng, H. M. L., & Ding, Z. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097068 · Cited by 2

Attribute Controlled Dialogue Prompting

Liu, R., Rashid, A., Kobyzev, I., Rezagholizadeh, M., & Poupart, P. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.150 · Cited by 2

Towards In-Context Non-Expert Evaluation of Reflection Generation for Counselling Conversations

Wu, Z., Balloccu, S., Helaoui, R., Recupero, D. R., & Riboni, D. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.9 · Cited by 2

Enhancements in BlenderBot 3: Expanding Beyond a Singular Model Governance and Boosting Generational Performance

Kobza, O., Herel, D., Čuhel, J., Gargiani, T., Pichl, J., Marek, P., Konrád, J., & Sedivy, J. (2023) · Future Internet · doi:10.3390/fi15120384 · Cited by 2

Requirements elicitation and response generation for conversational services

Zhang, B., Tu, Z., Wang, C., Sun, H., & Chu, D. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05454-6 · Cited by 2

Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries

Shrivastava, A., Dhole, K., Bhatt, A., & Raghunath, S. (2021) · doi:10.18653/v1/2021.acl-short.13 · Cited by 2

Cue-word Driven Neural Response Generation with a Shrinking VocabularyPreprint

Wang, Q., Liu, Y., Lv, C., Wang, Z., & Fu, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04927 · Cited by 2

"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue ResponsesPreprint

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12820 · Cited by 2

Semantic-Enhanced Explainable Finetuning for Open-Domain DialoguesPreprint

Zheng, Y., Wang, Y., Ke, P., Yang, Z., & Huang, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.03065 · Cited by 3

"Feels like I've known you forever": empathy and self-awareness in human open-domain dialogsPreprint

Lee, Y. K., Cho, W. I., Bae, S., Choi, H., Park, J., Kim, N. S., & Hahn, S. (2022) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/9qptj · Cited by 2

Conversational Human–Machine Interfaces *

Rodríguez‐Sánchez, M. J., Benghazi, K., Griol, D., & Callejas, Z. (2023) · Wiley eBooks · doi:10.1002/9781119863663.ch11 · Cited by 2

Proceedings of the Third Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)

(2022) · doi:10.18653/v1/2022.sustainlp-1 · Cited by 2

Towards Immersive Computational Storytelling:Card-Framework for Enhanced Persona-Driven DialoguesPreprint

Liao, B., & Vargas, D. V. (2023) · doi:10.36227/techrxiv.24605667.v1 · Cited by 2

Towards LLMCI - Multimodal AI for LLM-Vision UI OperationPreprint

Barham, H., & Fasha, M. (2024) · Research Square · doi:10.21203/rs.3.rs-4653823/v1 · Cited by 2

DA-BERT: Enhancing Knowledge Selection in Dialog via Domain Adapted BERT with Dynamic Masking Probability

Zeng, Z., Chow, C.-Y., & Li, N. (2021) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp52413.2021.00040 · Cited by 1

Designing rule-based conversational agents with behavioral programming: a study of human subjects

Rosenfeld, A., & Haimovich, N. (2022) · EuroMed Journal of Business · doi:10.1108/emjb-09-2021-0144 · Cited by 1

CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages

Kumar, S. H., Su, H., Manuvinakurike, R., Pinaroc, M., Prasad, S., Sahay, S., & Nachman, L. (2022) · Workshop on Speech and Language Processing for Assistive Technologies · doi:10.18653/v1/2022.slpat-1.9 · Cited by 3

Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk

Lưu, A. (2022) · Annual Meeting of the Association for Computational Linguistics: Student Research Workshop (ACL SRW) · doi:10.18653/v1/2022.acl-srw.14 · Cited by 1

MidMed: Towards Mixed-Type Dialogues for Medical Consultation

Shi, X., Liu, Z., Wang, C., Leng, H., Xue, K., Zhang, X., & Zhang, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.453 · Cited by 7

InterviewBot: Real-Time End-to-End Dialogue System for Interviewing Students for College Admission

Wang, Z., Keyes, N., Crawford, T., & Choi, J. D. (2023) · Information · doi:10.3390/info14080460 · Cited by 3

Multi-Source Probing for Open-Domain Conversational Understanding

Li, Y., Zhou, H., Zhou, J., & Huang, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.769 · Cited by 1

DigiHuman: A Conversational Digital Human with Facial Expressions

Özacar, K., & Özacar, K. (2024) · Turkish Journal of Science and Technology · doi:10.55525/tjst.1301324 · Cited by 1

Çevrimiçi perakendecilikte sohbet robotu kullanımında etkileşim, güven antropomorfizm ve kullanım seviyesinin müşteri memnuniyetine etkisi

Ventura, K., & Karabaşak, T. (2024) · Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi · doi:10.33707/akuiibfd.1459114 · Cited by 4

KRGP: Knowledge-Based Response Generation with Persona

Kosenko, D., & Zharikova, D. (2024) · Journal of Mathematical Sciences · doi:10.1007/s10958-024-07432-2 · Cited by 1

eCommerce Search and Discovery

Kallumadi, S. T., & King, T. H. (2024) · ACM eBooks · doi:10.1145/3674127.3674140 · Cited by 1

An Interactive Evaluation Framework for Empathetic Response Generation

Lei, X., Li, C., He, L., & Lin, X. A. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887960 · Cited by 1

Using ontologies to contextualize queries to large language models

Андреевич, Л. П. (2025) · Ontology of Designing · doi:10.18287/2223-9537-2025-15-2-239-248 · Cited by 1

AgentTOD: A Task-Oriented Dialogue Agent with a Flexible and Adaptive API Calling Paradigm

Xu, H.-D., Mao, X.-L., Sun, F., Che, T.-Y., Xu, C., & Huang, H. (2025) · ACM Transactions on Information Systems · doi:10.1145/3745021 · Cited by 1

Black-Box Adversarial Attack on Dialogue Generation via Multi-Objective Optimization

Le, K. G., & Luong, N. H. (2025) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3712256.3726431 · Cited by 1

IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents

Mohanty, S., Arabzadeh, N., Tupini, A., Sun, Y., Skrynnik, A., Zholus, A., Côté, M.-A., & Kiseleva, J. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730300 · Cited by 3

A Survey on Integration of Empathy Cues Into Empathetic Text Generation

Montiel-Vázquez, E. C., Cruz, C. A., Uresti, J. A. R., González-Mendoza, M., & Gómez, R. (2025) · IEEE Access · doi:10.1109/access.2025.3597304 · Cited by 1

Applying Emotion-Cause Entailment for Help-Seeker Guidance in Emotional Support Conversations

Chang, J., & Wu, C. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3605971 · Cited by 1

Causal-ESC : Capture the Dynamics in Cause-and-Effect Detection for Emotional Support Conversation

Zhao, Z., Xu, Y., Zhao, S., & Sun, X. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3648885 · Cited by 1

Integrating Cause-Awareness for Improving Emotional Support in Conversational AI

Vu, H.-L., & Nguyen, C.-V. T. (2025) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse68178.2025.11309517 · Cited by 1

CEREAL: personality-driven LLM-based conversational recommendation dataset with contextually-enriched and realistic user interactions

Lee, J., Kim, J., & Kang, P. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21183-z · Cited by 1

Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation

Li, B., Weng, Y., Ma, Z., Sun, B., & Shutao Li, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17189-5_15 · Cited by 1

COMEX: A Multi-task Benchmark for Knowledge-grounded COnversational Media EXploration

Tun, Z. Y., Speggiorin, A., Dalton, J., & Stamper, M. (2022) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3543829.3543830 · Cited by 1

Keyword-Aware Transformers Network for Chinese Open-Domain Conversation Generation

Zhou, Y., Zhi, C., Feng, X., Cui, W., Wang, H., Qin, A. H., Chen, X.-D., Wang, Y., & Huang, X. (2023) · Electronics · doi:10.3390/electronics12051228 · Cited by 1

Which Factors Predict the Chat Experience of a Natural Language Generation Dialogue Service?

Chen, E. (2023) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3583940 · Cited by 1

Dynalogue: A Transformer-Based Dialogue System with Dynamic Attention

Zhang, R., Wu, T., Chen, X., Wen, S., Nepal‬, ‪., Paris, C. L., & Xiang, Y. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583330 · Cited by 1

Foundation Models for Text Generation

Paaß, G., & Giesselbach, S. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-3-031-23190-2_6 · Cited by 3

Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generation

Liu, L., & Huang, J. X. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3592043 · Cited by 1

Aoba_v3 bot: a multimodal chatbot system combining rules and various response generation models

Moriya, S., Shiono, D., Fujihara, R., Kishinami, Y., Kimura, S., Sone, S., Akama, R., Matsumoto, Y., Suzuki, J., & Inui, K. (2023) · Advanced Robotics · doi:10.1080/01691864.2023.2240883 · Cited by 1

Distilled Language Models are economically efficient for the enterprise. ...mostly.

Howell, K., Christian, G., Fomitchov, P., Kehat, G., Marzulla, J., Rolston, L., Tredup, J., Zimmerman, I., Selfridge, E. O., & Bradley, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.24 · Cited by 2

Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation

Zeng, W., Zhao, L., He, K., Geng, R., Wang, J., Wu, W., & Xu, W. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.793 · Cited by 4

CausalDialogue: Modeling Utterance-level Causality in Conversations

Tuan, Y.-L., Albalak, A., Xu, W., Saxon, M., Pryor, C., Getoor, L. C., & Wang, W. Y. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.792 · Cited by 2

Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue

Wen, X., Wei, W., & Mao, X.-L. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.87 · Cited by 1

What Was Your Name Again? Interrogating Generative Conversational Models For Factual Consistency Evaluation

Lotfi, E., Bruyn, M. D., Buhmann, J., & Daelemans, W. M. P. (2022) · Workshop on Generation, Evaluation and Metrics (GEM) · doi:10.18653/v1/2022.gem-1.47 · Cited by 2

MERCY: Multiple Response Ranking Concurrently in Realistic Open-Domain Conversational Systems

Ghazarian, S., Hedayatnia, B., Jin, D., Liu, S., Peng, N., Liu, Y., & Hakkani-Tür, D. Z. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.58 · Cited by 1

Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models

Bae, S., Kwak, D., Kim, S., Ham, D., Kang, S., Lee, S., & Park, W. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.155 · Cited by 31

Deep Chit-Chat: Deep Learning for Chatbots

Wu, W., & Yan, R. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3320084 · Cited by 7

PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning

Bao, S., Huang, H., Wang, F., Wu, H. R., Wang, H., Wu, W., Guo, Z., Liu, Z., & Xu, X. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.222 · Cited by 118

Towards open domain spoken dialogue systems

Young, S. K. (2016) · Cambridge University Engineering Department Publications Database

A Survey for Efficient Open Domain Question Answering

Zhang, Q., Chen, S., Xu, D., Cao, Q., Chen, X., Cohn, T., & Fang, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.808 · Cited by 54