SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References
This work addresses the problem of expensive and unreliable QA evaluation for researchers and practitioners, offering an incremental improvement over existing transformer-based metrics.
The paper tackles the challenge of evaluating question answering systems by proposing SQuARRE, a metric that uses multiple positive and negative reference answers, and shows it outperforms previous baselines with the highest correlation to human annotations across various datasets.
Evaluation of QA systems is very challenging and expensive, with the most reliable approach being human annotations of correctness of answers for questions. Recent works (AVA, BEM) have shown that transformer LM encoder based similarity metrics transfer well for QA evaluation, but they are limited by the usage of a single correct reference answer. We propose a new evaluation metric: SQuArE (Sentence-level QUestion AnsweRing Evaluation), using multiple reference answers (combining multiple correct and incorrect references) for sentence-form QA. We evaluate SQuArE on both sentence-level extractive (Answer Selection) and generative (GenQA) QA systems, across multiple academic and industrial datasets, and show that it outperforms previous baselines and obtains the highest correlation with human annotations.