Learning to Represent Words in Context with Multilingual Supervision

Kazuya Kawakami, Chris Dyer

Feb 17, 2016 (modified: Feb 17, 2016) ICLR 2016 workshop submission readers: everyone
  • CMT id: 317
  • Abstract: We present a neural network architecture based on bidirectional LSTMs to compute representations of words in the sentential contexts. These context-sensitive word representations are suitable for, e.g., distinguishing different word senses and other context-modulated variations in meaning. To learn the parameters of our model, we use cross-lingual supervision, hypothesizing that a good representation of a word in context will be one that is sufficient for selecting the correct translation into a second language. We evaluate the quality of our representations as features in three downstream tasks: prediction of semantic supersenses (which assign nouns and verbs into a few dozen semantic classes), low resource machine translation, and a lexical substitution task, and obtain state-of-the-art results on all of these.
  • Conflicts: cs.cmu.edu

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