@inproceedings{jiang-etal-2021-im,
title = "{``}{I}{'}m Not Mad{''}: Commonsense Implications of Negation and Contradiction",
author = "Jiang, Liwei and
Bosselut, Antoine and
Bhagavatula, Chandra and
Choi, Yejin",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.346",
doi = "10.18653/v1/2021.naacl-main.346",
pages = "4380--4397",
abstract = "Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., {``}I{'}m mad at you{''}), humans can reason about the varying shades of contradictory statements ranging from straightforward negations ({``}I{'}m not mad at you{''}) to commonsense contradictions ({``}I{'}m happy{''}). Moreover, these negated or contradictory statements shift the commonsense implications of the original premise in interesting and nontrivial ways. For example, while {``}I{'}m mad{''} implies {``}I{'}m unhappy about something,{''} negating the premise does not necessarily negate the corresponding commonsense implications. In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions. We introduce ANION, a new commonsense knowledge graph with 624K if-then rules focusing on negated and contradictory events. We then present joint generative and discriminative inference models for this new resource, providing novel empirical insights on how logical negations and commonsense contradictions reshape the commonsense implications of their original premises.",
}
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<abstract>Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., “I’m mad at you”), humans can reason about the varying shades of contradictory statements ranging from straightforward negations (“I’m not mad at you”) to commonsense contradictions (“I’m happy”). Moreover, these negated or contradictory statements shift the commonsense implications of the original premise in interesting and nontrivial ways. For example, while “I’m mad” implies “I’m unhappy about something,” negating the premise does not necessarily negate the corresponding commonsense implications. In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions. We introduce ANION, a new commonsense knowledge graph with 624K if-then rules focusing on negated and contradictory events. We then present joint generative and discriminative inference models for this new resource, providing novel empirical insights on how logical negations and commonsense contradictions reshape the commonsense implications of their original premises.</abstract>
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%0 Conference Proceedings
%T “I’m Not Mad”: Commonsense Implications of Negation and Contradiction
%A Jiang, Liwei
%A Bosselut, Antoine
%A Bhagavatula, Chandra
%A Choi, Yejin
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F jiang-etal-2021-im
%X Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., “I’m mad at you”), humans can reason about the varying shades of contradictory statements ranging from straightforward negations (“I’m not mad at you”) to commonsense contradictions (“I’m happy”). Moreover, these negated or contradictory statements shift the commonsense implications of the original premise in interesting and nontrivial ways. For example, while “I’m mad” implies “I’m unhappy about something,” negating the premise does not necessarily negate the corresponding commonsense implications. In this paper, we present the first comprehensive study focusing on commonsense implications of negated statements and contradictions. We introduce ANION, a new commonsense knowledge graph with 624K if-then rules focusing on negated and contradictory events. We then present joint generative and discriminative inference models for this new resource, providing novel empirical insights on how logical negations and commonsense contradictions reshape the commonsense implications of their original premises.
%R 10.18653/v1/2021.naacl-main.346
%U https://aclanthology.org/2021.naacl-main.346
%U https://doi.org/10.18653/v1/2021.naacl-main.346
%P 4380-4397
Markdown (Informal)
[“I’m Not Mad”: Commonsense Implications of Negation and Contradiction](https://aclanthology.org/2021.naacl-main.346) (Jiang et al., NAACL 2021)
ACL
- Liwei Jiang, Antoine Bosselut, Chandra Bhagavatula, and Yejin Choi. 2021. “I’m Not Mad”: Commonsense Implications of Negation and Contradiction. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4380–4397, Online. Association for Computational Linguistics.