DeepAI AI Chat
Log In Sign Up

GTM: A Generative Triple-Wise Model for Conversational Question Generation

by   Lei Shen, et al.

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.


page 1

page 2

page 3

page 4


Conversational QA Dataset Generation with Answer Revision

Conversational question–answer generation is a task that automatically g...

OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach

Large-scale question-answer (QA) pairs are critical for advancing resear...

From Rewriting to Remembering: Common Ground for Conversational QA Models

In conversational QA, models have to leverage information in previous tu...

Modeling Semantic Relationship in Multi-turn Conversations with Hierarchical Latent Variables

Multi-turn conversations consist of complex semantic structures, and it ...

Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders

Asking good questions in large-scale, open-domain conversational systems...

QAConv: Question Answering on Informative Conversations

This paper introduces QAConv, a new question answering (QA) dataset that...

DoQA – Accessing Domain-Specific FAQs via Conversational QA

The goal of this work is to build conversational Question Answering (QA)...