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Prompting for explanations improves Adversarial NLI. Is this true? Yes it is true because it weakens superficial cues

Explanation prompts ask language models to not only assign a particular label to a giveninput, such as true, entailment, or …

Pride Kavumba, Ana Brassard, Benjamin Heinzerling, Kentaro Inui

因果的プロンプトによる NLI の敵対的ロバスト性の強化

Pride Kavumba, Ana Brassard, Benjamin Heinzerling, 坂口慶祐, 乾健太郎

COPA-SSE: Semi-structured Explanations for Commonsense Reasoning

We present Semi-Structured Explanations for COPA (COPA-SSE), a new crowdsourced dataset of 9,747 semi-structured, English common sense …

Ana Brassard, Benjamin Heinzerling, Pride Kavumba, Kentaro Inui

Are Prompt-based Models Clueless?

Finetuning large pre-trained language models with a task-specific head has advanced the state-of-the-art on many natural language …

Pride Kavumba, Ryo Takahashi, Yusuke Oda

プロンプトモデルは表面的手がかりを利用するか

Pride Kavumba, 高橋諒, 小田悠介

Learning to Learn to be Right for the Right Reasons

Improving model generalization on held-out data is one of the core objectives in common- sense reasoning. Recent work has shown that …

Pride Kavumba, Benjamin Heinzerling, Ana Brassard, Kentaro Inui

None the wiser? Adding “None’’Mitigates Superficial Cues in Multiple-Choice Benchmarks

Pride Kavumba, Ana Brassard, Benjamin Heinzerling, Naoya Inoue, Kentaro Inui

Improving Evidence Detection by Leveraging Warrants

Recognizing the implicit link between a claim and a piece of evidence (i.e. warrant) is the key to improving the performance of …

Keshav Singh, Paul Reisert, Naoya Inoue, Pride Kavumba, Kentaro Inui

When Choosing Plausible Alternatives, Clever Hans can be Clever

Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA. …

Pride Kavumba, Naoya Inoue, Benjamin Heinzerling, Keshav Singh, Paul Reisert, Kentaro Inui

Exploring Supervised Learning of Hierarchical Event Embedding with Poincaré Embeddings

Pride Kavumba, Naoya Inoue, Kentaro Inui