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2020 SEMEVAL SemEval 2020

UAlberta at SemEval-2020 Task 2: Using Translations to Predict Cross-Lingual Entailment

Abstract

AbstractWe investigate the hypothesis that translations can be used to identify cross-lingual lexical entailment. We propose novel methods that leverage parallel corpora, word embeddings, and multilingual lexical resources. Our results demonstrate that the implementation of these ideas leads to improvements in predicting entailment.

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Bradley Hauer , Amir Ahmad Habibi , Yixing Luan , Arnob Mallik , Grzegorz Kondrak

Topics

Natural Language Processing > Understanding > Semantic Analysis Natural Language Processing > Resources & Methods > Multilingual NLP

Keywords

parallel corpus word embedding multilingual lexical resource cross-lingual lexical entailment
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