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2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

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.

"I'm Not Mad": Commonsense Implications of Negation and Contradiction

technical paper

CoRT: Complementary Rankings from Transformers

MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding

On Learning Text Style Transfer with Direct Rewards

Focused Attention Improves Document-Grounded Generation

NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints

Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge

Progressive Generation of Long Text with Pretrained Language Models

SOCCER: An Information-Sparse Discourse State Tracking Collection in the Sports Commentary Domain

Identifying Medical Self-Disclosure in Online Communities

MultiOpEd: A Corpus of Multi-Perspective News Editorials

https://d3smihljt9218e.cloudfront.net/transcript/13681/subtitle/05237147175a037aae6077a66d369cc6.vtt

English (automatic)

"I'm Not Mad": Commonsense Implications of Negation and Contradiction

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