Cited by 19Speech and dialogue systemsLogic, Reasoning, and KnowledgeNatural Language Processing Techniques

CIDER: Commonsense Inference for Dialogue Explanation and Reasoning

Open access
Ghosal, Deepanway, Hong, Pengfei, Shen, Siqi, Majumder, Navonil, Mihalcea, Rada, Poria, Soujanya·2021·Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Abstract

Well, I missed several buses.How on earth can you miss several buses?I, ah ..., I have got late.But there's a bus every ten minutes, and you are over 1 hour late.Have you got it now?

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18653/v1/2021.sigdial-1.33

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • There are some reasoning works in the area of dialogue, but few in reducing hallucinations.

    “There are some reasoning works in the area of dialogue [39, 83, 272], but few in reducing hallucinations.”

    From Survey of Hallucination in Natural Language Generation (2023) · cites this paper as [83]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 21

  1. Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 2007. Dbpedia: A nucleus for a web of open data. In The semantic web, pages 722-735. Springer.
  2. Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015. A large anno- tated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326. · arXiv
  3. Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165. · arXiv
  4. Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam Hruschka, and Tom Mitchell. 2010. Toward an architecture for never-ending language learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 24.
  5. Ernest Davis. 2014. Representations of commonsense knowledge. Morgan Kaufmann.
  6. Herbert P. Grice. 1975. Logic and conversation. Speech acts, pages 41-58.
  7. Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith. 2018. Annotation artifacts in natural language inference data. arXiv preprint arXiv:1803.02324. · arXiv
  8. Jena D Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, and Yejin Choi. 2020. Comet-atomic 2020: On symbolic and neural commonsense knowledge graphs. arXiv preprint arXiv:2010.05953. · arXiv
  9. Mandar Joshi, Omer Levy, Luke Zettlemoyer, and Daniel S Weld. 2019. Bert for coreference reso- lution: Baselines and analysis. In Proceedings of the 2019 Conference on Empirical Methods in Nat- ural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5807-5812.
  10. Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017. Dailydialog: A manu- ally labelled multi-turn dialogue dataset. In Proceed- ings of the Eighth International Joint Conference on Natural Language Processing, IJCNLP 2017, Taipei, Taiwan, November 27 -December 1, 2017 -Volume 1: Long Papers, pages 986-995. Asian Federation of Natural Language Processing.
  11. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining ap- proach. arXiv preprint arXiv:1907.11692. · arXiv
  12. Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016. A cor- pus and cloze evaluation for deeper understanding of commonsense stories. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 839-849, San Diego, California. Association for Computational Linguis- tics. · DOI · Paper page
  13. Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David Buchanan, Lauren Berkowitz, Or Biran, and Jennifer Chu-Carroll. 2020. GLUCOSE: GeneraL- ized and COntextualized story explanations. In Pro- ceedings of the 2020 Conference on Empirical Meth- ods in Natural Language Processing (EMNLP), On- line.
  14. Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018. Hypothesis only baselines in natural language infer- ence. arXiv preprint arXiv:1805.01042. · arXiv
  15. Colin Raffel, Noam Shazeer, Adam Roberts, Kather- ine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to- text transformer. Journal of Machine Learning Re- search, 21(140):1-67.
  16. Maarten Sap, Ronan Le Bras, Emily Allaway, Chan- dra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi. 2019. Atomic: An atlas of machine commonsense for if- then reasoning. In Proceedings of the AAAI Con- ference on Artificial Intelligence, volume 33, pages 3027-3035.
  17. Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. Conceptnet 5.5: An open multilingual graph of gen- eral knowledge. In Proceedings of the AAAI Confer- ence on Artificial Intelligence, volume 31.
  18. Pontus Stenetorp, Sampo Pyysalo, Goran Topić, Tomoko Ohta, Sophia Ananiadou, and Jun'ichi Tsu- jii. 2012. brat: a web-based tool for NLP-assisted text annotation. In Proceedings of the Demonstra- tions Session at EACL 2012, Avignon, France. Asso- ciation for Computational Linguistics.
  19. Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie. 2019. Dream: A challenge data set and models for dialogue-based reading compre- hension. Transactions of the Association for Com- putational Linguistics, 7:217-231.
  20. Adina Williams, Nikita Nangia, and Samuel R Bow- man. 2017. A broad-coverage challenge corpus for sentence understanding through inference. arXiv preprint arXiv:1704.05426. · arXiv
  21. Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018. Swag: A large-scale adversarial dataset for grounded commonsense inference. arXiv preprint arXiv:1808.05326. · arXiv

Cited by

Sorted by citations

Authors
Ghosal, D., Hong, P., Shen, S., Majumder, N., Mihalcea, R., & Poria, S.
Affiliations
Singapore University of Technology and Design (SG)
Venue
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
Published
2021
Pages
301-313
OpenAlex
W3168838873
Cited by
19
References
4
Citations by year
2026: 2 · 2024: 6 · 2023: 6 · 2022: 5
FWCI
1.96
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Annual Meeting of the Special Interest Group on Discourse and Dialogue (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 › Logic, Reasoning, and Knowledge
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
commonsense reasoning, dialogue understanding, natural language inference

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 ›

Are you an author, or do you see a problem with this page? Request a review