Abstract
Users frequently ask simple factoid questions when encountering question answering (QA) systems, attenuating the impact of myriad recent works designed to support more complex questions. Prompting users with automatically generated suggested questions (SQs) can improve understanding of QA system capabilities and thus facilitate using this technology more effectively. While question generation (QG) is a well-established problem, existing methods are not targeted at producing SQ guidance for human users seeking more in-depth information about a specific concept. In particular, existing QG works are insufficient for this task as the generated questions frequently (1) require access to supporting documents as comprehension context (e.g., How many points did LeBron score?) and (2) focus on short answer spans, often producing peripheral factoid questions unlikely to attract interest. In this work, we aim to generate self-explanatory questions that focus on the main document topics and are answerable with variable length passages as appropriate. We satisfy these requirements by using a BERT-based Pointer-Generator Network (BertPGN) trained on the Natural Questions (NQ) dataset. First, we show that the BertPGN model produces state-of-the-art QG performance for long and short answers for in-domain NQ (BLEU-4 for 20.13 and 28.09, respectively). Secondly, we evaluate this QG model on the out-of-domain NewsQA dataset automatically and with human evaluation, demonstrating that our method produces better SQs for news articles, even those from a different domain than the training data.
Abstract (translated)
URL
https://arxiv.org/abs/2010.09692
