On the Importance of Word Boundaries in Character-level Neural Machine Translation

Duygu Ataman
Duygu Ataman
Mattia Antonino Di Gangi
Mattia Antonino Di Gangi

NGT@EMNLP-IJCNLP, pp. 187-193, 2019.

Cited by: 2|Bibtex|Views66|DOI:https://doi.org/10.18653/v1/D19-5619
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Other Links: dblp.uni-trier.de|arxiv.org

Abstract:

Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality. The standard approach to overcome this limitation is to segment words into subword units, typically using some external tools wi...More

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