Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation (2023)

Abstract: Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast into two different settings: answer-aware and answer-unaware. While the former facilitates the models by exposing the expected answer, the latter is more realistic and receiving growing attentions recently. What-to-ask and how-to-ask are the two main challenges in the answer-unaware setting. To address the first challenge, existing methods mainly select sequential sentences in context as the rationales. We argue that the conversation generated using such naive heuristics may not be natural enough as in reality, the interlocutors often talk about the relevant contents that are not necessarily sequential in context. Additionally, previous methods decide the type of question to be generated (boolean/span-based) implicitly. Modeling the question type explicitly is crucial as the answer, which hints the models to generate a boolean or span-based question, is unavailable. To this end, we present SG-CQG, a two-stage CQG framework. For the what-to-ask stage, a sentence is selected as the rationale from a semantic graph that we construct, and extract the answer span from it. For the how-to-ask stage, a classifier determines the target answer type of the question via two explicit control signals before generating and filtering. In addition, we propose Conv-Distinct, a novel evaluation metric for CQG, to evaluate the diversity of the generated conversation from a context. Compared with the existing answer-unaware CQG models, the proposed SG-CQG achieves state-of-the-art performance.

## introduction building systems that can comprehend human speech and provide assistance to humans through * contribution during the internship at institute for infocomm research. ## related work our work is closely related to two lines of prior work. extended related work is in appendix a.1. ## sg-cqg we formulate the answer-unaware conversational question generation (cqg) task as: figure 1 demonstrates an overview of our proposed framework. it consists of two main components: (1) a what-to-ask module aims to select a reasonable sentence in the referential context c as the current rationale r n and thereby a span in r n as the target answer a n , given d n . (2) a how-to-ask module aims to generate the question q n , guided by the rationale r n and target answer a n . ## main results to evaluate the performance of sg-cqg on the answer-unaware cqg task, we employ 4 baselines for comparison, as shown in ## conclusion this paper presents sg-cqg, a two-stage framework for the cqg task in the answer-unaware setting. firstly, the what-to-ask module aims to select a sentence as the rationale by the proposed semantic graph and extract the answer span from it. the how-to-ask module classifies the type of the question before generating and filtering it. additionally, we propose a set of automatic evaluation criteria for answer-unaware cqg, especially a novel metric, conv-distinct, to evaluate the generated conversation from a context. extensive automatic evaluation and human evaluation show that our method achieves state-of-the-art performances in the answer-unaware setting on coqa, with a significant improvement in the conversational alignment property compared to previous frameworks. in the future, we will focus on how to reason over our semantic graph to select the rationale, and further improve the performances of how-to-ask module.