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Linguistic knowledge-based vocabularies for Neural Machine Translation

Published online by Cambridge University Press:  02 July 2020

Noe Casas Open the ORCID record for Noe Casas [Opens in a new window] ,
Marta R. Costa-jussà ,
José A. R. Fonollosa ,
Juan A. Alonso  and
Ramón Fanlo
Noe Casas*
Affiliation:
TALP Research Center, Universitat Politècnica de Catalunya
Marta R. Costa-jussà
Affiliation:
TALP Research Center, Universitat Politècnica de Catalunya
José A. R. Fonollosa
Affiliation:
TALP Research Center, Universitat Politècnica de Catalunya
Juan A. Alonso
Affiliation:
Lucy Software, United Language Group
Ramón Fanlo
Affiliation:
Lucy Software, United Language Group
*
*Corresponding author. E-mail: contact@noecasas.com
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Abstract

Neural Networks applied to Machine Translation need a finite vocabulary to express textual information as a sequence of discrete tokens. The currently dominant subword vocabularies exploit statistically-discovered common parts of words to achieve the flexibility of character-based vocabularies without delegating the whole learning of word formation to the neural network. However, they trade this for the inability to apply word-level token associations, which limits their use in semantically-rich areas and prevents some transfer learning approaches e.g. cross-lingual pretrained embeddings, and reduces their interpretability. In this work, we propose new hybrid linguistically-grounded vocabulary definition strategies that keep both the advantages of subword vocabularies and the word-level associations, enabling neural networks to profit from the derived benefits. We test the proposed approaches in both morphologically rich and poor languages, showing that, for the former, the quality in the translation of out-of-domain texts is improved with respect to a strong subword baseline.

Keywords

machine translationneural networkmorphologyvocabulary
Type
Article
Information
Natural Language Engineering , Volume 27 , Issue 4 , July 2021 , pp. 485 - 506
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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