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Dialogue Natural Language Inference
Open accessAbstract
Consistency is a long standing issue faced by dialogue models. In this paper, we frame the consistency of dialogue agents as natural language inference (NLI) and create a new natural language inference dataset called Dialogue NLI. We propose a method which demonstrates that a model trained on Dialogue NLI can be used to improve the consistency of a dialogue model, and evaluate the method with human evaluation and with automatic metrics on a suite of evaluation sets designed to measure a dialogue model's consistency.
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“Recently, several works have proposed evaluation metrics for measuring consistency, such as using natural language inference (NLI) [58, 278], training learnable evaluation metrics [309], or releasing an additional test set for coherence [14].”
From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [278]
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Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue modelsPreprint
Smith, E. M., & Williams, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03300 · Cited by 33
peng, Y. T., & Lei, C. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1841 · Cited by 18
PoE: A Panel of Experts for Generalized Automatic Dialogue Assessment
Zhang, C., D’Haro, L. F., Zhang, Q., Friedrichs, T., & Li, H. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3250825 · Cited by 13
Tang, Y., Wang, B., Miao, F., Zhao, D., Huang, K., He, R., & Hou, Y. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.299 · Cited by 12
Learning Dialogue Representations from Consecutive Utterances
Zhou, Z., Zhang, D., Xiao, W., Dingwall, N., Ma, X., Arnold, A., & Xiang, B. (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.55 · Cited by 14
Leveraging Large Language Models for Automated Dialogue Analysis
Finch, S. E., Paek, E. S., & Choi, J. D. (2023) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2023.sigdial-1.20 · Cited by 19
Partner Personas Generation for Dialogue Response Generation
Lu, H., Lam, W. P., Cheng, H., & Meng, H. M. L. (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.382 · Cited by 10
Don’t Forget Your ABC’s: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems
Finch, S. E., Finch, J. D., & Choi, J. D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.839 · Cited by 20
Lu, Z., Wei, W., Qu, X., Mao, X.-L., Chen, D., & Chen, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.395 · Cited by 19
Probing Neural Dialog Models for Conversational Understanding
Saleh, A., Deutsch, T., Casper, S., Belinkov, Y., & Shieber, S. M. (2020) · Workshop on NLP for Conversational AI (NLP4ConvAI) · doi:10.18653/v1/2020.nlp4convai-1.15 · Cited by 10
SILT: Efficient transformer training for inter-lingual inference
Huertas‐Tato, J., Martín, A. G., & Camacho, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116923 · Cited by 15
Li, Z., Zhang, J., Fei, Z., Feng, Y., & Zhou, J. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.91 · Cited by 13
Qin, L., Xie, T., Huang, S., Chen, Q., Xiao, X., & Che, W. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.182 · Cited by 10
VIRT: Improving Representation-based Text Matching via Virtual Interaction
Li, D., Yang, Y., Tang, H., Liu, J., Wang, Q., Wang, J., Xu, T., Wu, W., & Chen, E. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.59 · Cited by 10
Dual Task Framework for Improving Persona-Grounded Dialogue Dataset
Kim, M., Kwak, B.-W., Kim, Y., Lee, H.-I., Hwang, S.-W., & Yeo, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21338 · Cited by 10
Metadata
- Authors
- Welleck, S., Weston, J., Szlam, A., & Cho, K.
- Affiliations
- New York University (US)
- Meta (United States) (US)
- Corresponding author
- Welleck, Sean
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 3731-3741
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2962989446
- Cited by
- 249
- References
- 27
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 15 · 2023: 73 · 2022: 48 · 2021: 54 · 2020: 30 · 2019: 8
- FWCI
- 13.39
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- natural language inference, dialogue agents, consistency improvement, human evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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