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arXiv · 1901.03735

EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference

Abhilasha Ravichander·Aakanksha Naik·Carolyn Rose·Eduard Hovy
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Abstract

Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle. We present EQUATE (Evaluating Quantitative Understanding Aptitude in Textual Entailment), a new framework for quantitative reasoning in textual entailment. We benchmark the performance of 9 published NLI models on EQUATE, and find that on average, state-of-the-art methods do not achieve an absolute improvement over a majority-class baseline, suggesting that they do not implicitly learn to reason with quantities. We establish a new baseline Q-REAS that manipulates quantities symbolically. In comparison to the best performing NLI model, it achieves success on numerical reasoning tests (+24.2%), but has limited verbal reasoning capabilities (-8.1%). We hope our evaluation framework will support the development of models of quantitative reasoning in language understanding.

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Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, Eduard Hovy. 2019-01-11. EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference. https://arxiv.org/abs/1901.03735

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