Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

EMNLP, pp. 670-680, 2017.

Cited by: 919|Bibtex|Views228|DOI:https://doi.org/10.18653/v1/D17-1070
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful. Several attempts at learning unsupervised representations of sentences have not reached satisfacto...More

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