Unsupervised Evaluation for Question Answering with Transformers
Lukas Muttenthaler, Isabelle Augenstein, Johannes Bjerva · 2020
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 model's 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.