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Sentiment Tagging with Partial Labels using Modular Architectures

2019-06-03ACL 2019Code Available0· sign in to hype

Xiao Zhang, Dan Goldwasser

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Abstract

Many NLP learning tasks can be decomposed into several distinct sub-tasks, each associated with a partial label. In this paper we focus on a popular class of learning problems, sequence prediction applied to several sentiment analysis tasks, and suggest a modular learning approach in which different sub-tasks are learned using separate functional modules, combined to perform the final task while sharing information. Our experiments show this approach helps constrain the learning process and can alleviate some of the supervision efforts.

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