Abstract
Defeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is a penguin). Though long recognized in classical AI and philosophy, defeasible inference has not been extensively studied in the context of contemporary data-driven research on natural language inference and commonsense reasoning. We introduce Defeasible NLI (abbreviated δ-NLI), a dataset for defeasible inference in natural language. δ-NLI contains extensions to three existing inference datasets covering diverse modes of reasoning: common sense, natural language inference, and social norms. From δ-NLI, we develop both a classification and generation task for defeasible inference, and demonstrate that the generation task is much more challenging. Despite lagging human performance, however, generative models trained on this data are capable of writing sentences that weaken or strengthen a specified inference up to 68% of the time.
Original language | English |
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Title of host publication | Findings of the Association for Computational Linguistics Findings of ACL |
Subtitle of host publication | EMNLP 2020 |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 4661-4675 |
Number of pages | 15 |
ISBN (Electronic) | 9781952148903 |
State | Published - 2020 |
Externally published | Yes |
Event | Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 - Virtual, Online Duration: 16 Nov 2020 → 20 Nov 2020 |
Publication series
Name | Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020 |
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Conference
Conference | Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 |
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City | Virtual, Online |
Period | 16/11/20 → 20/11/20 |
Bibliographical note
Publisher Copyright:© 2020 Association for Computational Linguistics
Funding
This work was supported by the Allen Institute for AI, the University of Washington, DARPA CwC through ARO (W911NF15-1-0543), DARPA MCS program through NIWC Pacific (N66001-19-2-4031), and DARPA KAIROS. The U.S. Gov- ernment is authorized to reproduce and distribute reprints for governmental purposes. The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsement.
Funders | Funder number |
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DARPA KAIROS | |
Army Research Office | W911NF15-1-0543, N66001-19-2-4031 |
Defense Advanced Research Projects Agency | |
University of Washington | |
ALLEN INSTITUTE |