Sentiment Tagging with Partial Labels using Modular Architectures

Xiao Zhang, Dan Goldwasser


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.
Anthology ID:
P19-1055
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
579–590
Language:
URL:
https://aclanthology.org/P19-1055
DOI:
10.18653/v1/P19-1055
Bibkey:
Cite (ACL):
Xiao Zhang and Dan Goldwasser. 2019. Sentiment Tagging with Partial Labels using Modular Architectures. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 579–590, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Sentiment Tagging with Partial Labels using Modular Architectures (Zhang & Goldwasser, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1055.pdf
Supplementary:
 P19-1055.Supplementary.pdf
Code
 cosmozhang/Modular_Neural_CRF