Disney Research
Disney Research

Multiplicative Representations for Unsupervised Semantic Role Induction

We propose a neural model to learn argument embeddings from the context by explicitly incorporating dependency relations as multiplicative factors, which bias argument embeddings according to their dependency roles.

  • August 7, 2016
  • Annual Meeting of the Association for Computational Linguistics 2016

Authors

  • Yi Luan (University of Washington, Disney Research Pittsburgh)
  • Yangfeng Ji (Georgia Institute of Technology)
  • Hannaneh Hajishirzi (University of Washington)
  • Boyang Albert Li Disney Research

Abstract

Multiplicative Representations for Unsupervised Semantic Role Induction-Image

In unsupervised semantic role labeling, identifying the role of an argument is usually informed by its dependency relation with the predicate. In this work, we propose a neural model to learn argument embeddings from the context by explicitly incorporating dependency relations as multiplicative factors, which bias argument embeddings according to their dependency roles. Our model outperforms existing state-of-the-art embeddings in unsupervised semantic role induction on the CoNLL 2008 dataset and the SimLex999 word similarity task. Qualitative results demonstrate our model can effectively bias argument embeddings based on their dependency role.

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