Unsupervised Evaluation for Question Answering with Transformers

It is challenging to automatically evaluate the answer of a QA model at inference time. Although many models provide confidence scores, and simple heuristics can go a long way towards indicating answer correctness, such measures are heavily dataset-dependent and are unlikely to generalise. In this work, we begin by investigating the hidden representations of questions, answers, and contexts in transformer-based QA architectures. We observe a consistent pattern in the answer representations, which we show can be used to automatically evaluate whether or not a predicted answer span is correct. Our method does not require any labelled data and outperforms strong heuristic baselines, across 2 datasets and 7 domains. We are able to predict whether or not a models answer is correct with 91.37 accuracy on SQuAD, and 80.7 accuracy on SubjQA. We expect that this method will have broad applications, e.g., in semi-automatic development of QA datasets.