Enhancing Question Generation with Commonsense Knowledge

Enhancing Question Generation with Commonsense Knowledge Xin Jia†, Hao Wang‡, Dawei Yin‡, Yunfang Wu†∗ †MOE Key Lab of Computational Linguistics, School of EECS, Peking University ‡Baidu Inc., China {jemmryx, wuyf}@pku.edu.cn way wh@yeah.net, yindawei@acm.org Abstract Question generation (QG) is to generate natural and grammatical questions that can be answered by a specific answer for a given context. Previous sequence-to-sequence models suffer from a problem that asking high-quality questions requires commonsense knowledge as backgrounds, which in most cases can not be learned directly from training data, resulting in unsatisfactory questions deprived of knowledge. In this paper, we propose a multi-task learning framework to introduce commonsense knowledge into question generation process. We first retrieve relevant commonsense knowledge triples from mature databases and select triples with the conversion information from source context to question. Based on these informative knowledge triples, we design two auxiliary tasks to incorporate commonsense knowledge into the main QG model, where one task is Concept Relation Classification and the other is Tail Concept Generation. Ex- perimental results on SQuAD show that our proposed methods are able to noticeably improve the QG performance on both automatic and human evaluation metrics, demonstrating that incor- porating external commonsense knowledge with multi-task learning can help the model generate human-like and high-quality questions. 1 Introduction Question Generation (QG) has become an essential task for NLP, which aims to generate grammatical and fluent questions for a given context and answer. QG can create question-answer pairs as data aug- mentation for Question Answering (QA) (Tang et al., 2017; Duan et al., 2017; Zhang and Bansal, 2019). Moreover, it is also useful in education (Heilman and Smith, 2010; Jia et al., 2020a) and business ap- plications (Mostafazadeh et al., 2016), such as creating materials for language beginners, helping build chatbots, etc. Existing question generation methods can be roughly grouped into two categories. First, rule-based methods utilize handcrafted paradigms to perform declarative-to-interrogative sentence transformations (Heilman and Smith, 2009; Dhole and Manning, 2020), but these methods often consume lots of efforts from domain experts and usually cover limited areas. Second, neural network-based methods typically model the question generation task in a fully data-driven manner (Du et al., 2017; Zhou et al., 2017; Wang et al., 2020a), which has made much progress in recent years. One key issue, however, is still up in the air: human beings often ask questions with commonsense knowledge that may exist in their brain but not appear in the given context. Take the instance in Table 1 as an example. To generate the human-like high-quality question, one must have the corresponding commonsense knowledge that the “European Parliament” and “Council of the European Union” are of “governing bodies”. Lacking such commonsense knowledge results in unsatisfactory questions that sim- ply copy some words from the source context. This directs us to introduce commonsense backgrounds to bridge the knowledge gap between the given contexts and generated questions. Actually, previous NLP works have investigated the structured commonsense knowledge to help text generation, such as story generation (Yang et al., 2019; …

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