2020
EMNLP
EMNLP 2020
Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?
Abstract
AbstractWe introduce four tasks designed to determine which sentence encoders best capture discourse properties of sentences from scientific abstracts, namely coherence and cohesion between clauses of a sentence, and discourse relations within sentences. We show that even if contextual encoders such as BERT or SciBERT encodes the coherence in discourse units, they do not help to predict three discourse relations commonly used in scientific abstracts. We discuss what these results underline, namely that these discourse relations are based on particular phrasing that allow non-contextual encoders to perform well.
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The Questioner
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Natural Language Processing
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Keyword Pioneer
— discourse properties
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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, Security & Privacy, Speech & Audio
Authors
Topics
Natural Language Processing > Understanding > Semantic Analysis
Natural Language Processing > Understanding > Syntax
Natural Language Processing > Resources & Methods > Text Representation
Deep Learning > Learning Types > Representation Learning
Artificial Intelligence > Core AI > Natural Language Processing