Unsupervised Evaluation for Question Answering with Transformers

• URL: http://arxiv.org/abs/2010.03222v1
• Date: Wed, 7 Oct 2020 07:03:30 GMT
• Title: Unsupervised Evaluation for Question Answering with Transformers
• Authors: Lukas Muttenthaler, Isabelle Augenstein, Johannes Bjerva
• Abstract summary: We investigate 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 is correct. We are able to predict whether or not a model's answer is correct with 91.37% accuracy SQuAD, and 80.7% accuracy on SubjQA.
• Score: 46.16837670041594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 generalize. 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 labeled 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 the semi-automatic development of QA datasets

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