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Conference Papers Year : 2019

Mining Discourse Markers for Unsupervised Sentence Representation Learning

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Abstract

Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data – such as discourse markers between sentences – mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as “coincidentally” or “amazingly”. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it’s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.
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Dates and versions

hal-02397473 , version 1 (06-12-2019)

Identifiers

  • HAL Id : hal-02397473 , version 1
  • OATAO : 24994

Cite

Damien Sileo, Tim van de Cruys, Camille Pradel, Philippe Muller. Mining Discourse Markers for Unsupervised Sentence Representation Learning. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2019), Jun 2019, Minneapolis, United States. pp.3477-3486. ⟨hal-02397473⟩
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