@inproceedings{mahler-etal-2017-breaking,
title = "Breaking {NLP}: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems",
author = "Mahler, Taylor and
Cheung, Willy and
Elsner, Micha and
King, David and
de Marneffe, Marie-Catherine and
Shain, Cory and
Stevens-Guille, Symon and
White, Michael",
editor = "Bender, Emily and
Daum{\'e} III, Hal and
Ettinger, Allyson and
Rao, Sudha",
booktitle = "Proceedings of the First Workshop on Building Linguistically Generalizable {NLP} Systems",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5405",
doi = "10.18653/v1/W17-5405",
pages = "33--39",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems
%A Mahler, Taylor
%A Cheung, Willy
%A Elsner, Micha
%A King, David
%A de Marneffe, Marie-Catherine
%A Shain, Cory
%A Stevens-Guille, Symon
%A White, Michael
%Y Bender, Emily
%Y Daumé III, Hal
%Y Ettinger, Allyson
%Y Rao, Sudha
%S Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F mahler-etal-2017-breaking
%X 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.
%R 10.18653/v1/W17-5405
%U https://aclanthology.org/W17-5405
%U https://doi.org/10.18653/v1/W17-5405
%P 33-39
Markdown (Informal)
[Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems](https://aclanthology.org/W17-5405) (Mahler et al., 2017)
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.