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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Cites
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5446 · Cited by 4,222
A large annotated corpus for learning natural language inference
Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1075 · Cited by 3,377
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Williams, A., Nangia, N., & Bowman, S. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1101 · Cited by 3,342
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte CarloPreprint
Homan, M. D., & Gelman, A. (2014) · arXiv (Cornell University) · Cited by 3,078
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1070 · Cited by 2,049
The PASCAL Recognising Textual Entailment Challenge
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(2018) · International Conference on Learning Representations · Cited by 1,621
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Cited by
Sorted by citations
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)
Dinan, E., Logacheva, V., Malykh, V., Miller, A., Shuster, K., Urbanek, J., Kiela, D., Szlam, A. D., Serban, I. V., Lowe, R., et al. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-29135-8_7 · Cited by 395
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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