Diversify Question Generation with Continuous Content Selectors and Question Type Modeling (2020)

Abstract: Generating questions based on answers and relevant contexts is a challenging task. Recent work mainly pays attention to the quality of a single generated question. However, question generation is actually a one-to-many problem, as it is possible to raise questions with different focuses on contexts and various means of expression. In this paper, we explore the diversity of question generation and come up with methods from these two aspects. Specifically, we relate contextual focuses with content selectors, which are modeled by a continuous latent variable with the technique of conditional variational auto-encoder (CVAE). In the realization of CVAE, a multimodal prior distribution is adopted to allow for more diverse content selectors. To take into account various means of expression, question types are explicitly modeled and a diversity-promoting algorithm is proposed further. Experimental results on public datasets show that our proposed method can significantly improve the diversity of generated questions, especially from the perspective of using different question types. Overall, our proposed method achieves a better trade-off between generation quality and diversity compared with existing approaches.

## introduction as a reverse task of question answering (qa), question generation (qg) aims to generate questions from a given answer and its relevant context. the task holds the potential value of educational purpose to generate questions for reading comprehension materials @xcite . it can also be deployed as chatbot components @xcite for evaluating or improving mental health @xcite . moreover, qg can be applied to extend the question-answer pairs @xcite for qa systems. traditional methods for qg mainly use rigid heuristic rules to transform a sentence into related source context: the network was engineered and operated by mci telecommunications under a cooperative agreement with the nsf . target question: who operated the vbsn network? focus 1: the network was engineered and operated by mci telecommunications under a cooperative agreement with the nsf . (ours) who operates the network with nsf ? focus 2: the network was engineered and operated by mci telecommunications under a cooperative agreement with the nsf . (ours) who operated the network under a cooperative agreement with the nsf ? focus 3: the network was engineered and operated by mci telecommunications under a cooperative agreement with the nsf . (ours) in what company was the network engineered and operated by the nsf ? figure 1 : diversified questions generated by our method for the given passage-answer pair (answer is underlined). different questions can be raised according to distinct focuses on the context (colored) and various means of expression (italic). however, given an answer and its associated context, it is possible to raise multiple questions with different focuses on the context and various means of expression. figure 1 shows some different questions that can be generated from a given source context. the characteristic of diversity is inherent in qg and has the potential to enhance the value of this task. however, the diversity is not fully explored with existing methods. @xcite and @xcite noticed this problem and modeled the variety with latent variable models. however, the introduced latent variable was regarded as a holistic attribute, whose meaning was opaque and weakly related to the origin of diversity. more recently, @xcite proposed a mixture content selection model for generation, whose diversity is determined by a fixed number of selectors. however, the discrete property confines its variety to a large extent. in this paper, we use a more flexible continuous latent variable for content selection to deal with different focuses on a context. moreover, question types are explicitly incorporated to consider different ways of expression. with these components, a question can be generated in three steps. firstly, a content selector in the form of a continuous latent variable is sampled conditioning on the source context. secondly, a question type is predicted based on the context as well as the content selector. lastly, the content of a question is generated with above information about contextual focuses and means of expression. considering the variety of content selectors and question types, the diversity of generated questions can be ensured. overall, the main contributions of this paper are as follows: ## related work automatic question generation has attracted an increasing attention from the natural language gen-eration community in recent years, which is reflected in newly published datasets @xcite and sophisticated techniques @xcite . traditional methods are mainly rule-based, where they first transform the source information into syntactic representation and then use templates to generate related questions @xcite . these methods largely depend on rigid heuristic rules and cannot be easily generalized. in contrast to rule-based methods, neural networks have the potential to learn implicit patterns from labeled data, thus become more prevalent in question generation. @xcite and @xcite followed the paradigm of sequenceto-sequence and showed promising results when combining rich features and attention mechanism. sun et al. (2018) and zhou et al. (2019) incorporated answer-focused information to improve the relevance between answers and questions. @xcite and @xcite introduced graph networks to estimate significant contents in the source context. most of previous work regarded question generation as a one-to-one problem and focused on improving the quality of a single generated question. some work noticed the diversity inherent in qg and came up with methods to consider this characteristic. @xcite used a latent variable to model the holistic attributes in questions. similar ideas could also been found in some related work @xcite . however, the meaning of the holistic features is only opaque and cannot be strongly connected with diversity. more recently, @xcite proposed a mixture content selection model for generation. the diversity was determined by a fixed number of content selectors. different from their work, we model the latent variable of content selectors in a continuous space, which holds the potential of capturing more variety inherent in content selection. besides above related work, other techniques plugged into the general encoder-decoder framework can also be utilized to promote diversity @xcite . however, the particular characteristics of question generation are not fully considered in these approaches. ## method question generation aims to model the probability of a question q given an answer a and its context c, recognition network prior network z ' z u1 question type u2 um xn x1 y1 y2 ym h1 h2 hn x2 o1 o2 on xn x1 x2 v1 v2 vm hn ' h2 ' h1 ' y1 ym-1 q t content selector question type predictor focused encoder & focused decoder which can be combined as the source information @xmath0, a}. to diversify generated questions, we incorporate a continuous multi-dimensional latent variable z for content selection and explicitly model question types to deal with means of expression. generation can be factorized into three stages. firstly, a content selector z is sampled conditioning on the input x. this is used to indicate which parts of the source information should be focused on. secondly, a question type q t is predicted considering the specific content selector z and the input x. lastly, the relevant question content q c is generated with selected contents and predicted question type. the final question q can be composed as (q t , q c ). the factorization can be formulated as follows: the overall architecture is illustrated in figure 2 . in the following subsections, we will elaborate the details of each stage. ## conclusion in this paper, we explicitly diversify the question generation from the perspectives of contextual focuses and means of expression. we model focuses through continuous content selectors and introduce a multimodal prior to allow for more diverse selectors. we consider various means of expression through the modeling of question types and a related diversity-promoting algorithm. on public datasets, our approach achieves the best trade-off between generation quality and diversity. further analysis also demonstrates the effectiveness of our proposed model components.