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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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Ren, F., Liu, Y., Li, B., Liu, S., Wang, B., Wang, J., Liu, C., & Ma, Q. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3546190 · Cited by 1
Neundlinger, K., Frankus, E., Häufler, I., Layer-Wagner, T., Kriglstein, S., & Schrank, B. (2023) · Digitale Gesellschaft · doi:10.14361/9783839465646 · Cited by 1
IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection
Deng, J., Dai, H., Guo, X., Ju, Y., & Peng, W. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.584 · Cited by 1
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
PASTA: A Dataset for Modeling PArticipant STAtes in Narratives
Ghosh, S., Koupaee, M., Chen, I., Ferraro, F., Chambers, N., & Balasubramanian, N. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00600 · Cited by 5
Contrastive Learning for Inference in Dialogue
Ishii, E., Xu, Y., Wilie, B., Ji, Z., Lovenia, H., Chung, W., & Fung, P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.631 · Cited by 1
Exploring Continual Learning of Compositional Generalization in NLI
Fu, X., & Frank, A. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00680 · Cited by 4
Takayama, J., Kajiwara, T., & Arase, Y. (2022) · Journal of Natural Language Processing · doi:10.5715/jnlp.29.84 · Cited by 1
Madureira, B., & Schlangen, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-short.73 · Cited by 1
AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models
Ryb, S., Giulianelli, M., Sinclair, A., & Fernández, R. (2022) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/2022.starsem-1.5 · Cited by 1
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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