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Learning to Represent Bilingual Dictionaries
2019
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Bilingual word embeddings have been widely used to capture the correspondence of lexical semantics in different human languages. However, the cross-lingual correspondence between sentences and words is less studied, despite that this correspondence can significantly benefit many applications such as crosslingual semantic search and textual inference. To bridge this gap, we propose a neural embedding model that leverages bilingual dictionaries 1 . The proposed model is trained to map the lexical
doi:10.18653/v1/k19-1015
dblp:conf/conll/ChenTCCSZ19
fatcat:acovrv3wuzbtnnifgysnrkc63q