A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, Richard Socher

Nov 04, 2016 (modified: Nov 19, 2016) ICLR 2017 conference submission readers: everyone
  • Abstract: Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in a single model. We introduce such a joint many-task model together with a strategy for successively growing its depth to solve increasingly complex tasks. All layers include shortcut connections to both word representations and lower-level task predictions. We use a simple regularization term to allow for optimizing all model weights to improve one task's loss without exhibiting catastrophic interference of the other tasks. Our single end-to-end trainable model obtains state-of-the-art results on chunking, dependency parsing, semantic relatedness and textual entailment. It also performs competitively on POS tagging. Our dependency parsing layer relies only on a single feed-forward pass and does not require a beam search.
  • TL;DR: A single deep multi-task learning model for five different NLP tasks.
  • Keywords: Natural language processing, Deep learning
  • Conflicts: logos.t.u-tokyo.ac.jp, salesforce.com

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