Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems

Taylor Mahler, Willy Cheung, Micha Elsner, David King, Marie-Catherine de Marneffe, Cory Shain, Symon Stevens-Guille, Michael White


Abstract
This paper describes our “breaker” submission to the 2017 EMNLP “Build It Break It” shared task on sentiment analysis. In order to cause the “builder” systems to make incorrect predictions, we edited items in the blind test data according to linguistically interpretable strategies that allow us to assess the ease with which the builder systems learn various components of linguistic structure. On the whole, our submitted pairs break all systems at a high rate (72.6%), indicating that sentiment analysis as an NLP task may still have a lot of ground to cover. Of the breaker strategies that we consider, we find our semantic and pragmatic manipulations to pose the most substantial difficulties for the builder systems.
Anthology ID:
W17-5405
Original:
W17-5405v1
Version 2:
W17-5405v2
Volume:
Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Emily Bender, Hal Daumé III, Allyson Ettinger, Sudha Rao
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
33–39
Language:
URL:
https://aclanthology.org/W17-5405
DOI:
10.18653/v1/W17-5405
Bibkey:
Cite (ACL):
Taylor Mahler, Willy Cheung, Micha Elsner, David King, Marie-Catherine de Marneffe, Cory Shain, Symon Stevens-Guille, and Michael White. 2017. Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems. In Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems, pages 33–39, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems (Mahler et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5405.pdf