EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference
Abhilasha Ravichander, Aakanksha Naik, Carolyn Penstein Rosé, Eduard H. Hovy · 2019
Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle.We present EQUATE 1 (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.