We propose a generative adversarial training approach for the problem of clarification question generation. Our approach generates clarification questions with the goal of eliciting new information that would make the given context more complete. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate on two datasets, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training.
 A goal of natural language processing is to develop techniques that enable machines to process naturally occurring language. However, not all language is clear and, as humans, we may not always understand each other BID10 ; in cases of gaps or mismatches in knowledge, we tend to ask questions BID9 . In this work, we focus on the task of automatically generating clarification questions: questions that ask for information that is missing from a given linguistic context. Our clarification question generation model builds on the sequence-to-sequence approach that has proven effective for several language generation tasks BID37 BID39 BID5 . Unfortunately, training a sequence-to-sequence model directly on context/question pairs yields generated questions that are highly generic 1 , corroborating a common finding in dialog systems BID17 . Our goal is to be able to generate questions that are useful and specific.To achieve this, we begin with a recent observation of BID30 , who considered the task of question reranking: the system should learn to generate clarification questions whose answers have high utility, which they defined as the likelihood that this question would lead to an answer that will make the context more complete ( §2.3). Inspired by this, we construct a question generation model that first generates a question given a context, and then generates a hypothetical answer to that question. Given this (context, question, answer) tuple, we train a utility calculator to estimate the usefulness of this question. We then show that this utility calculator can be generalized using ideas for generative adversarial networks BID8 for text BID40 , wherein the utility predictor plays the role of the "discriminator" and the question generator is the "generator" ( §2.2), which we train using the MIXER algorithm BID29 .We evaluate our approach on two question generation datasets: for posts on Stack Exchange and for Amazon product descriptions (Figure 1 ). Using both automatic metrics and human evaluation, we demonstrate that our adversarially trained model generates a more diverse set of questions than all the baseline models. Furthermore , we find that although all models generate questions that are relevant to the context at hand, our adversarially-trained model generates questions that are more specific to the context. In this work, we describe a novel approach to the problem of clarification question generation. Given a context, we use the observation of BID30 that the usefulness of a clarification question can be measured by the value of updating the context with an answer to the question. We use a sequence-to-sequence model to generate a question given a context and a second sequenceto-sequence model to generate an answer given the context and the question. Given the (context, predicted question, predicted answer) triple we calculator the utility of this triple and use it as a reward to retrain the question generator using reinforcement learning based MIXER model. Further, to improve upon the utility function, we reinterpret it as a discriminator in an adversarial setting and train both the utility function and the MIXER model in a minimax fashion. We find that our adversarial training approach produces more diverse questions compared to both a model trained using maximum likelihood objective and a model trained using utility reward based reinforcement learning. There are several avenues of future work in this area. Following BID24 , we could combine text input with image input to generate more relevant questions. Because some questions can be answered by looking at the product image in the Amazon dataset BID22 , this could help generate more relevant and useful questions. As in most One significant research challenge in the space of free text generation problems when the set of possible outputs is large, is that of automatic evaluation BID20 : in our results we saw some correlation between human judgments and automatic metrics, but not enough to trust the automatic metrics completely. Lastly, integrating such a question generation model into a real world platform like StackExchange or Amazon to understand the real utility of such models and to unearth additional research questions.
We propose an adversarial training approach to the problem of clarification question generation which uses the answer to the question to model the reward. 
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