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.

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 question given a source text, focusing on a specific answer span. To our knowledge, all works tackling QG have thus far focused exclusively on generating relevant questions which can be answered given the source text: for instance, given AAAI was founded in 1979 as input, a question likely to be automatically generated would be When was AAAI founded?, where the answer 1979 is a span of the input. Such questions are useful to evaluate reading comprehension for both machines BIBREF0, BIBREF1 and humans BIBREF2. However, the human ability of asking questions goes well beyond evaluation: asking questions is essential in education BIBREF3 and has been proven to be fundamental for children cognitive development BIBREF4. 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 also tightly linked to one's understanding of the context, e.g. a teacher testing a student's knowledge by asking questions whose answers require a deeper understanding of the context and more complex reasoning. From an applicative point of view, we deem the ability to generate curious, inquisitive, questions as highly beneficial for a broad range of scenarios: i) in the context of human-machine interaction (e.g. robots, chat-bots, educational tools), where the communication with the users could be more natural; ii) during the learning process itself, which could be partially driven in a self-supervised manner, reminiscent of how humans learn by exploring and interacting with their environment. To our knowledge, this is the first paper attempting to tackle Curiosity-driven neural question generation. The contributions of this paper can be summarized as follow: we propose a new natural language generation task: curiosity-driven question generation; we propose a method to derive data for the task from popular non-conversational QA datasets; we experiment using language model pre-training and reinforcement learning, on two different datasets; we report a human evaluation analysis to assess both the pertinence of the automatic metrics used and the efficacy of the proposed dataset-creation method above.

Question: What are the four forums the data comes from?
Answer: Darkode,  Hack Forums, Blackhat and Nulled.