Understand
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.
Built on
Equate: A benchmark evaluation framework for quantitative reasoning in natural language inference
Abhilasha Ravichander, Aakanksha Naik, Carolyn Penstein Rosé, and Eduard H. Hovy. 2019 · 1901
Earlier work this paper cites.
Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli. 2019 · 1904
Earlier work this paper cites.
Ernie: Enhanced representation through knowledge integration
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu. 2019 · 1904
Earlier work this paper cites.
Solving general arithmetic word problems
Subhro Roy and Dan Roth. 2015 · 2015
Earlier work this paper cites.
Reasoning about quantities in natural language
Subhro Roy, Tim Vieira, and Dan Roth. 2015 · 2015
Earlier work this paper cites.
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Then
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