The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
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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Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
“Two possible types of inconsistency occur in open-domain dialogue generation: (1) self-inconsistency [147, 159] or incoherence [8, 29] among the system utterances, such as when the system contradicts its previous utterance; (2) external inconsistency with an external source, such as factually incorrect utterances.”
BackgroundInfluentialOn Faithfulness and Factuality in Abstractive Summarization
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.173 · Cited by 1,185
(2020) · doi:10.18653/v1/2020.acl-main.450 · Cited by 356
Self-citationThe Second Conversational Intelligence Challenge (ConvAI2)
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Self-citationChallenges in building intelligent open-domain dialog systems
Huang, M., Zhu, X., & Gao, J. (2020) · ACM Transactions on Information Systems · doi:10.1145/3383123 · Cited by 288
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
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