Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension

Daniel Andor, Luheng He, Kenton Lee, Emily Pitler


Abstract
Reading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers. We enable a BERT-based reading comprehension model to perform lightweight numerical reasoning. We augment the model with a predefined set of executable ‘programs’ which encompass simple arithmetic as well as extraction. Rather than having to learn to manipulate numbers directly, the model can pick a program and execute it. On the recent Discrete Reasoning Over Passages (DROP) dataset, designed to challenge reading comprehension models, we show a 33% absolute improvement by adding shallow programs. The model can learn to predict new operations when appropriate in a math word problem setting (Roy and Roth, 2015) with very few training examples.
Anthology ID:
D19-1609
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5947–5952
Language:
URL:
https://aclanthology.org/D19-1609
DOI:
10.18653/v1/D19-1609
Bibkey:
Cite (ACL):
Daniel Andor, Luheng He, Kenton Lee, and Emily Pitler. 2019. Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5947–5952, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension (Andor et al., EMNLP 2019)
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PDF:
https://aclanthology.org/D19-1609.pdf
Attachment:
 D19-1609.Attachment.zip
Data
DROP