Dialogue Natural Language Inference
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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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Several works have proposed evaluation metrics for measuring consistency, such as using natural language inference, training learnable evaluation metrics, or releasing an additional test set for coherence.
“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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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte CarloPreprint
Homan, M. D., & Gelman, A. (2014) · arXiv (Cornell University) · Cited by 2,964 · see citing works
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 · see citing works
The PASCAL Recognising Textual Entailment Challenge
Dagan, I., Glickman, O., & Magnini, B. (2006) · Lecture notes in computer science · doi:10.1007/11736790_9 · Cited by 1,674 · see citing works
(2018) · International Conference on Learning Representations · Cited by 1,621 · see citing works
Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400 · see citing works
Enhanced LSTM for Natural Language Inference
Chen, Q., Zhu, X., Ling, Z.-H., Wei, S., Jiang, H., & Inkpen, D. Z. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1152 · Cited by 1,186 · see citing works
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1205 · Cited by 1,177 · see citing works
Self-citationAnnotation Artifacts in Natural Language Inference Data
Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., & Smith, N. A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2017 · Cited by 907 · see citing works
A Persona-Based Neural Conversation Model
Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1094 · Cited by 898 · see citing works
Pyro: Deep Universal Probabilistic ProgrammingPreprint
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F. H., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., & Goodman, N. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.09538 · Cited by 767 · see citing works
Hypothesis Only Baselines in Natural Language Inference
Poliak, A., Naradowsky, J., Haldar, A., Rudinger, R., & Durme, B. V. (2018) · International Workshop on Semantic Evaluation · doi:10.18653/v1/s18-2023 · Cited by 685 · see citing works
A SICK cure for the evaluation of compositional distributional semantic models
Marelli, M., Menini, S., Baroni, M., Bentivogli, L., Raffaella Bernardi, B., & Zamparelli, R. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/39qdhuevzbqa · Cited by 680 · see citing works
SciTaiL: A Textual Entailment Dataset from Science Question Answering
Khot, T., Sabharwal, A., & Clark, P. E. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12022 · Cited by 416 · see citing works
ParlAI: A Dialog Research Software Platform
Miller, A., Feng, W., Batra, D., Bordes, A., Fisch, A., Lu, J., Parikh, D., & Weston, J. (2017) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d17-2014 · Cited by 326 · see citing works
Self-citationTraining Millions of Personalized Dialogue Agents
Mazaré, P.-E., Humeau, S., Raison, M., & Bordes, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1298 · Cited by 226 · see citing works
An extended model of natural logic
MacCartney, B., & Manning, C. D. (2009) · doi:10.3115/1693756.1693772 · Cited by 192 · see citing works
Transforming Question Answering Datasets Into Natural Language Inference DatasetsPreprint
Demszky, D., Guu, K., & Liang, P. (2018) · arXiv.org · doi:10.48550/arxiv.1809.02922 · Cited by 192 · see citing works
Lan, W., & Xu, W. H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04330 · Cited by 113 · see citing works
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation
Poliak, A., Haldar, A., Rudinger, R., Hu, J. E., Pavlick, E., White, A. S., & Durme, B. V. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5441 · Cited by 50 · see citing works
Poliak, A., Belinkov, Y., Glass, J. R., & Durme, B. V. (2018) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n18-2082 · Cited by 43 · see citing works
Importance of a Search Strategy in Neural Dialogue ModellingPreprint
Kulikov, I., Miller, A., Cho, K., & Weston, J. (2018) · arXiv (Cornell University) · Cited by 37 · see citing works
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Flagged first, then by citations
1 of the 27 references couldn’t be matched to a known paper.
Cited by
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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 · see citing works
“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 · see citing works
(2020) · doi:10.18653/v1/2020.acl-main.450 · Cited by 356 · see citing works
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 362 · see citing works
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 · see citing works
Recent advances in deep learning based dialogue systems: a systematic survey
Ni, J., Young, T., Pandelea, V., Xue, F., & Cambria, E. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10248-8 · Cited by 279 · see citing works
Personalizing Dialogue Agents via Meta-Learning
Madotto, A., Lin, Z., Wu, C.-S., & Fung, P. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1542 · Cited by 183 · see citing works
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
Miura, Y., Zhang, Y., Tsai, E., Langlotz, C., & Jurafsky, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.416 · Cited by 149 · see citing works
Don’t Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training
Li, M., Roller, S., Kulikov, I., Welleck, S., Boureau, Y.-L., Cho, K., & Weston, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.428 · Cited by 128 · see citing works
Self-citationEntity-level Factual Consistency of Abstractive Text Summarization
Nan, F., Nallapati, R. M., Wang, Z., dos Santos, C. N., Zhu, H., Zhang, D., McKeown, K. R., & Xiang, B. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.235 · Cited by 127 · see citing works
Honovich, O., Choshen, L., Aharoni, R., Neeman, E., Szpektor, I., & Abend, O. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.619 · Cited by 113 · see citing works
MuTual: A Dataset for Multi-Turn Dialogue Reasoning
Cui, L., Wu, Y., Liu, S., Zhang, Y., & Zhou, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.130 · Cited by 110 · see citing works
BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data
Song, H., Wang, Y., Zhang, K., Zhang, W., & Liu, T. (2021) · doi:10.18653/v1/2021.acl-long.14 · Cited by 105 · see citing works
Evaluating Coherence in Dialogue Systems using Entailment
Dziri, N., Kamalloo, E., Mathewson, K. W., & Zai͏̈ane, O. R. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1381 · Cited by 91 · see citing works
Babu, A., & Boddu, S. B. (2024) · Exploratory Research in Clinical and Social Pharmacy · doi:10.1016/j.rcsop.2024.100419 · Cited by 93 · see citing works
Song, H., Wang, Y., Zhang, W., Liu, X., & Liu, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.516 · Cited by 87 · see citing works
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Maharana, A., Lee, D.-H., Tulyakov, S., Bansal, M., Barbieri, F., & Fang, Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.747 · Cited by 96 · see citing works
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition
Jeretič, P., Warstadt, A., Bhooshan, S., & Williams, A. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.768 · Cited by 83 · see citing works
One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles
Ma, Z., Dou, Z., Zhu, Y., Zhong, H., & Wen, J.-R. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462828 · Cited by 78 · see citing works
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Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., & Reitter, D. (2023) · Computational Linguistics · doi:10.1162/coli_a_00486 · Cited by 71 · see citing works
Generating Persona Consistent Dialogues by Exploiting Natural Language Inference
Song, H., Zhang, W., Hu, J., & Liu, T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6417 · Cited by 71 · see citing works
McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00567 · Cited by 70 · see citing works
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation
Pang, B., Nijkamp, E., Han, W., Zhou, L., Liu, Y., & Tu, K. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.333 · Cited by 70 · see citing works
McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2021) · arXiv · doi:10.48550/arxiv.2111.09509 · Cited by 70 · see citing works
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Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., Cohen, V., Scialom, T., Szpektor, I., Hassidim, A., & Matias, Y. (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.287 · Cited by 75 · see citing works
FaithDial : A Faithful Benchmark for Information-Seeking Dialogue
Dziri, N., Kamalloo, E., Milton, S., Zai͏̈ane, O. R., Yu, M., Ponti, E. M., & Reddy, S. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00529 · Cited by 68 · see citing works
LM vs LM: Detecting Factual Errors via Cross Examination
Cohen, R., Hamri, M., Geva, M., & Globerson, A. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.778 · Cited by 60 · see citing works
Kim, H., Kim, B., & Kim, G. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.65 · Cited by 50 · see citing works
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation
Zheng, C., Sabour, S., Wen, J., Zhang, Z., & Huang, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.99 · Cited by 49 · see citing works
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Nie, Y., Williamson, M., Bansal, M., Kiela, D., & Weston, J. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.134 · Cited by 58 · see citing works
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Chen, J., Choi, E., & Durrett, G. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.324 · Cited by 51 · see citing works
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DynaEval: Unifying Turn and Dialogue Level Evaluation
Zhang, C., Chen, Y., D’Haro, L. F., Zhang, Y., Friedrichs, T., Lee, G., & Li, H. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.441 · Cited by 52 · see citing works
What’s in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization
Adams, G., Alsentzer, E., Ketenci, M., Zucker, J. E., & Elhadad, N. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.382 · Cited by 45 · see citing works
A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation
Cao, Y., Bi, W., Fang, M., Shi, S., & Tao, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.550 · Cited by 40 · see citing works
Nye, M., Tessler, M. H., Tenenbaum, J. B., & Lake, B. M. (2021) · arXiv · doi:10.48550/arxiv.2107.02794 · Cited by 40 · see citing works
Mishra, A., Patel, D., Vijayakumar, A., Li, X. L., Kapanipathi, P., & Talamadupula, K. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.104 · Cited by 39 · see citing works
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Roller, S., Boureau, Y.-L., Weston, J., Bordes, A., Dinan, E., Fan, A., Gunning, D., Ju, D., Li, M., Poff, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2006.12442 · Cited by 38 · see citing works
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Zhang, A., Tay, Y., Zhang, S., Chan, A., Luu, A. T., Hui, S. C., & Fu, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08597 · Cited by 36 · see citing works
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Shi, W., Li, Y., Sahay, S., & Yu, Z. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.295 · Cited by 25 · see citing works
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Pathak, A., Srihari, R. K., & Natu, N. (2021) · Computational and Mathematical Organization Theory · doi:10.1007/s10588-021-09336-x · Cited by 19 · see citing works
Training Social Skills in Virtual Reality Machine Learning as a Process of Co-Creation
Neundlinger, K., Mühlegger, M., Kriglstein, S., Layer-Wagner, T., & Regal, G. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-93780-5_10 · Cited by 19 · see citing works
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Fung, Y. R., Chakrabarty, T., Guo, H., Rambow, O., Muresan, S., & Ji, H. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.941 · Cited by 26 · see citing works
Empathetic Chatbot Enhancement and Development: A Literature Review
Wardhana, A. K., Ferdiana, R., & Hidayah, I. (2021) · IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) · doi:10.1109/aims52415.2021.9466027 · Cited by 18 · see citing works
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Varshney, N., Mishra, S. R., & Baral, C. R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.240 · Cited by 17 · see citing works
Measuring Attribution in Natural Language Generation Models
Rashkin, H., Nikolaev, V. A., Lamm, M., Aroyo, L. M., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., & Reitter, D. (2023) · Computational Linguistics · doi:10.1162/coli_a_00490 · Cited by 16 · see citing works
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Varshney, N., & Baral, C. R. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.756 · Cited by 19 · see citing works
Hu, L., Zhang, X., Song, D., Zhou, C., He, H., & Nie, L. (2025) · ACM Transactions on Information Systems · doi:10.1145/3711857 · Cited by 18 · see citing works
Li, H., Song, Y., & Fan, 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.429 · Cited by 15 · see citing works
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Eleftheriadis, P., Perikos, I., & Hatzilygeroudis, I. (2023) · Applied Sciences · doi:10.3390/app13042577 · Cited by 15 · see citing works
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Majumder, B. P., Berg-Kirkpatrick, T., McAuley, J., & Jhamtani, H. (2021) · doi:10.18653/v1/2021.acl-short.74 · Cited by 15 · see citing works
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Miura, Y., Zhang, Y., Tsai, E., Langlotz, C., & Jurafsky, D. (2020) · North American Chapter of the Association for Computational Linguistics · doi:10.48550/arxiv.2010.10042 · Cited by 264 · see citing works
Transferable Persona-Grounded Dialogues via Grounded Minimal Edits
Wu, C., Zheng, Y., Mao, X., & Huang, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.183 · Cited by 14 · see citing works
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Chia, Y. K., Bing, L., Aljunied, S. M., Si, L., & Poria, S. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.688 · Cited by 15 · see citing works
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues
Wang, H., Hu, M., Deng, Y., Wang, R., Mi, F., Wang, W., Wang, Y., Kwan, W.-C., King, I., & Wong, K. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.641 · Cited by 14 · see citing works
Shi, T., & Song, Y. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3105584 · Cited by 14 · see citing works
Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding
Storks, S., Gao, Q., Zhang, Y., & Chai, J. Y. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.422 · Cited by 14 · see citing works
Zhong, W., Wang, S., Tang, D., Xu, Z., Guo, D., Chen, Y., Wang, J., Yin, J., Zhou, M., & Duan, N. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.177 · Cited by 14 · see citing works
A logical-based corpus for cross-lingual evaluation
Salvatore, F. D. S., Finger, M., & Hirata, R. (2019) · Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo) · doi:10.18653/v1/d19-6103 · Cited by 17 · see citing works
Singh, G. V., Firdaus, M., Shambhavi, Mishra, S., & Ekbal, A. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108900 · Cited by 13 · see citing works
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He, Q., Wang, H., & Zhang, Y. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.447 · Cited by 13 · see citing works
(2021) · arXiv (Cornell University) · Cited by 13 · see citing works
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Song, H., Wang, Y., Zhang, K., Zhang, W., & Liu, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06169 · Cited by 13 · see citing works
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 23 · see citing works
peng, Y. T., & Lei, C. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1841 · Cited by 14 · see citing works
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 12 · see citing works
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 · see citing works
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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 11 · see citing works
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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 12 · see citing works
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 · see citing works
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 10 · see citing works
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 12 · see citing works
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 · see citing works
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 12 · see citing works
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 11 · see citing works
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 · see citing works
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 · see citing works
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 9 · see citing works
- 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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