no code implementations • WS 2019 • Keshav Singh, Paul Reisert, Naoya Inoue, Pride Kavumba, Kentaro Inui
Recognizing the implicit link between a claim and a piece of evidence (i. e. warrant) is the key to improving the performance of evidence detection.
no code implementations • WS 2019 • Pride Kavumba, Naoya Inoue, Benjamin Heinzerling, Keshav Singh, Paul Reisert, Kentaro Inui
Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA.
no code implementations • NAACL 2021 • Pride Kavumba, Benjamin Heinzerling, Ana Brassard, Kentaro Inui
Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and hard test set without superficial cues.
1 code implementation • LREC 2022 • Ana Brassard, Benjamin Heinzerling, Pride Kavumba, Kentaro Inui
We present Semi-Structured Explanations for COPA (COPA-SSE), a new crowdsourced dataset of 9, 747 semi-structured, English common sense explanations for Choice of Plausible Alternatives (COPA) questions.
no code implementations • ACL 2022 • Pride Kavumba, Ryo Takahashi, Yusuke Oda
However, models with a task-specific head require a lot of training data, making them susceptible to learning and exploiting dataset-specific superficial cues that do not generalize to other datasets.
no code implementations • EACL 2023 • Pride Kavumba, Ana Brassard, Benjamin Heinzerling, Kentaro Inui
Explanation prompts ask language models to not only assign a particular label to a giveninput, such as true, entailment, or contradiction in the case of natural language inference but also to generate a free-text explanation that supports this label.
Ranked #1 on Natural Language Inference on ANLI test