Multi-turn dialogue-oriented pretrained question generation model
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ORIGINAL ARTICLE
Multi-turn dialogue-oriented pretrained question generation model Yanmeng Wang1,2 · Wenge Rong1,2
· Jianfei Zhang1,2 · Shijie Zhou1,2 · Zhang Xiong1,2
Received: 17 February 2020 / Accepted: 25 April 2020 © The Author(s) 2020
Abstract In recent years, teaching machines to ask meaningful and coherent questions has attracted considerable attention in natural language processing. Question generation has found wide applications in areas such as education (testing knowledge) and chatbots (enhancing interaction). Following previous studies on conversational question generation, we propose a pretrained, encoder–decoder model that can incorporate the semantic information from both passage and hidden conversation representations. We adopt BERT as the encoder to combine external text and dialogue history, and we design a multi-head attention-based decoder to incorporate the semantic information from both text and hidden dialogue representations into the decoding process, thereby generating coherent questions. Experiments with conversational question generation and document-grounded dialogue response generation tasks indicate that the proposed model is superior to baseline models in terms of both standard metrics and human evaluations. Keywords Multi-turn dialogue · Question generation · Pretrained language model · Text passage
Introduction Recently, question generation (QG) has attracted considerable interest. Its purpose is to generate human-like questions from a given sentence or paragraph [7]. Owing to its complexity and ambiguity, QG is a highly challenging problem in natural language processing. Specifically, unlike one-toone mapping tasks such as machine translation, QG involves significant diversity in the space of reasonable questions that may be obtained from a given descriptive text [10]. Current studies on QG use paragraphs and answers as input to predict questions [6,10]. Most QG datasets lack
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Wenge Rong [email protected] Yanmeng Wang [email protected] Jianfei Zhang [email protected] Shijie Zhou [email protected] Zhang Xiong [email protected]
1
State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
2
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
conversational context, whereas humans normally gather information or test knowledge through conversations involving a series of interconnected questions and answers, thus alleviating ambiguity [28]. To addresses this issue, Gao et al. recently identified a new QG challenge called conversation QG (CQG) [11]. In this task, a system should be able to generate a series of interconnected questions depending on a given passage, and participate in a question-answering-style conversation as the questioner. Table 1 presents an example of such a task. In this conversation, a questioner and a respondent talk about a passage, and this implies that the content of the passage is important in predicting this conversation. The task here is to evaluate the capabil
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