Ask to Learn: A Study on Curiosity-driven Question Generation

Proceedings of the 28th International Conference on Computational Linguistics, pages 2224–2235 Barcelona, Spain (Online), December 8-13, 2020 2224 Ask to Learn: A Study on Curiosity-driven Question Generation Thomas Scialom reciTAL, Paris, France Sorbonne Universit´e, CNRS, LIP6 thomas@recital.ai Jacopo Staiano reciTAL, Paris, France jacopo@recital.ai Abstract We propose a novel text generation task, namely Curiosity-driven Question Generation. We start from the observation that the Question Generation task has traditionally been considered as the dual problem of Question Answering, hence tackling the problem of generating a question given the text that contains its answer. Such questions can be used to evaluate machine reading comprehension. However, in real life, and especially in conversational settings, humans tend to ask questions with the goal of enriching their knowledge and/or clarifying aspects of previously gathered information. We refer to these inquisitive questions as Curiosity-driven: these questions are generated with the goal of obtaining new information (the answer) which is not present in the input text. In this work, we experiment on this new task using a conversational Question Answering (QA) dataset; further, since the majority of QA dataset are not built in a conversational manner, we describe a methodology to derive data for this novel task from non-conversational QA data. We investigate several automated metrics to measure the different properties of Curious Questions, and experiment different approaches on the Curiosity-driven Question Generation task, including model pre-training and reinforcement learning. Finally, we report a qualitative evaluation of the generated outputs. 1 Introduction The growing interest in Machine Reading Comprehension (MRC) has sparked significant research efforts on Question Generation (QG), the dual task to Question Answering (QA). In QA, the objective is to produce an adequate response given a query and a text; conversely, for QG, the task is generally defined as generating relevant questions given a source text and, optionally, a specific target answer included therein. To our knowledge, all works tackling QG have thus far exclusively focused on generating relevant questions which can be answered given the source text: for instance, given The 1st COLING conference took place in 1965 as input, a question likely to be automatically generated would be When did the 1st COLING conference take place?, where the answer 1965 is a span of the input. Such questions are useful to evaluate reading comprehension for both machines (Hermann et al., 2015; Eyal et al., 2019) and humans (Mani et al., 1999). However, the human ability of asking questions goes well beyond evaluation: asking questions is essential in education (Gall, 1970) and has been proven to be fundamental for children cognitive de- velopment (Chouinard et al., 2007). Curiosity is baked into the human experience: it allows to extend one’s comprehension and knowledge by asking questions that, while being relevant to context, are not directly answerable by it, thus being inquisitive and curious. The significance of such kind of questions is two-fold: first, they allow for gathering novel relevant information, e.g. a student asking for clarification; second, they are tightly linked to one’s understanding of the …

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