GTM : A Generative Triple-wise Model for Conversational Question Generation (2021)

Abstract: Generating some appealing questions in open-domain conversations is an effective way to improve human-machine interactions and lead the topic to a broader or deeper direction. To avoid dull or deviated questions, some researchers tried to utilize answer, the “future” information, to guide question generation. However, they separate a post-question-answer (PQA) triple into two parts: post-question (PQ) and question-answer (QA) pairs, which may hurt the overall coherence. Besides, the QA relationship is modeled as a one-to-one mapping that is not reasonable in open-domain conversations. To tackle these problems, we propose a generative triple-wise model with hierarchical variations for open-domain conversational question generation (CQG). Latent variables in three hierarchies are used to represent the shared background of a triple and one-to-many semantic mappings in both PQ and QA pairs. Experimental results on a large-scale CQG dataset show that our method significantly improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines.

## introduction questioning in open-domain dialogue systems is indispensable since a good system should have the ability to well interact with users by not only responding but also asking @xcite . besides, raising questions is a proactive way to guide users to go deeper and further into conversations @xcite . therefore, the ultimate goal of opendomain conversational question generation (cqg) is to enhance the interactiveness and maintain the continuity of a conversation @xcite . cqg differs fundamentally from traditional question generation (tqg) @xcite @xcite that generates a question given a sentence/paragraph/passage and a specified answer within it. while in cqg, an answer always follows the to-be-generated question, and is unavailable during inference @xcite . at the same time, each utterance in open-domain scenario is casual and can be followed by several appropriate sentences, i.e., one-to-many mapping @xcite . at first, the input information of cqg was mainly a given post @xcite , and the generated questions were usually dull or deviated (q3 and q4 in an open-domain pqa often takes place under a background that can be inferred from all utterances in the triple and help enhance the overall coherence. when it comes to the semantic relationship in each triple, the content of a specific question is under the control of its post and answer @xcite . meanwhile, either a post or an answer could correspond to several meaningful questions. as shown in on these grounds, we propose a generative triplewise model (gtm) for cqg. specifically, we firstly introduce a triple-level variable to capture the shared background among pqa. then, two separate variables conditioned on the triple-level variable are used to represent the latent space for question and answer, and the question variable is also dependent on the answer one. during training, the latent variables are constrained to reconstruct both the original question and answer according to the hierarchical structure we define, making sure the triple-wise relationship flows through the latent variables without any loss. for the question generation process, we sample the triple-level and answer variable given a post, then obtain the question variable conditioned on them, and finally generate a question based on the post, triple-level and question variables. experimental results on a largescale cqg dataset show that gtm can generate more fluent, coherent, and intriguing questions for open-domain conversations. the main contribution is threefold: ## proposed model given a post as the input, the goal of cqg is to generate the corresponding question. following the work of @xcite and @xcite , we leverage the question type qt to control the generated question, and take advantage of the answer information a to improve coherence. in training set, each conversation is represented as {p, q, qt, a}, consisting of post @xmath0, question @xmath1, and answer @xmath2. ## experiments in this section, we conduct experiments to evaluate our proposed method. we first introduce some empirical settings, including dataset, hyperparameters, baselines, and evaluation measures. then we illustrate our results under both automatic and human evaluations. finally, we give out some cases generated by different models and do further analyses over our method. ## related work the researches on open-domain dialogue systems have developed rapidly @xcite @xcite , and our work mainly touches two fields: open-domain conversational question generation (cqg), and context modeling in dialogue systems. we introduce these two fields as follows and point out the main differences between our method and previous ones. ## conclusion we propose a generative triple-wise model for generating appropriate questions in open-domain conversations, named gtm. gtm models the entire background in a triple and one-to-many mappings in pq and qa pairs simultaneously with latent variables in three hierarchies. it is trained in a onestage end-to-end framework without pre-training like the previous state-of-the-art model that also takes answer into consideration. experimental results on a large-scale cqg dataset show that gtm can generate fluent, coherent, informative as well as intriguing questions.