Sentiment Tagging with Partial Labels using Modular Architectures

Xiao Zhang     Dan Goldwasser    
Association for Computational Linguistics (ACL), 2019


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.

Bib Entry

    author = "Xiao Zhang and Dan Goldwasser",
    title = "Sentiment Tagging with Partial Labels using Modular Architectures",
    booktitle = "Association for Computational Linguistics (ACL)",
    year = "2019"