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Learning Multilingual Word Representations using a Bag-of-Words Autoencoder

Stanislas Lauly,A. Boulanger,H. Larochelle

Published 2014 in arXiv.org

ABSTRACT

Recent work on learning multilingual word representations usually relies on the use of word-level alignements (e.g. infered with the help of GIZA++) between translated sentences, in order to align the word embeddings in different languages. In this workshop paper, we investigate an autoencoder model for learning multilingual word representations that does without such word-level alignements. The autoencoder is trained to reconstruct the bag-of-word representation of given sentence from an encoded representation extracted from its translation. We evaluate our approach on a multilingual document classification task, where labeled data is available only for one language (e.g. English) while classification must be performed in a different language (e.g. French). In our experiments, we observe that our method compares favorably with a previously proposed method that exploits word-level alignments to learn word representations.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    arXiv.org

  • Publication date

    2014-01-08

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1401.1803
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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