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

by   Shaobo Cui, et al.

Large-scale question-answer (QA) pairs are critical for advancing research areas like machine reading comprehension and question answering. To construct QA pairs from documents requires determining how to ask a question and what is the corresponding answer. Existing methods for QA pair generation usually follow a pipeline approach. Namely, they first choose the most likely candidate answer span and then generate the answer-specific question. This pipeline approach, however, is undesired in mining the most appropriate QA pairs from documents since it ignores the connection between question generation and answer extraction, which may lead to incompatible QA pair generation, i.e., the selected answer span is inappropriate for question generation. However, for human annotators, we take the whole QA pair into account and consider the compatibility between question and answer. Inspired by such motivation, instead of the conventional pipeline approach, we propose a model named OneStop generate QA pairs from documents in a one-stop approach. Specifically, questions and their corresponding answer span is extracted simultaneously and the process of question generation and answer extraction mutually affect each other. Additionally, OneStop is much more efficient to be trained and deployed in industrial scenarios since it involves only one model to solve the complex QA generation task. We conduct comprehensive experiments on three large-scale machine reading comprehension datasets: SQuAD, NewsQA, and DuReader. The experimental results demonstrate that our OneStop model outperforms the baselines significantly regarding the quality of generated questions, quality of generated question-answer pairs, and model efficiency.


page 1

page 2

page 3

page 4


Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering

Question answering (QA) is an important use case on voice assistants. A ...

Variational Question-Answer Pair Generation for Machine Reading Comprehension

We present a deep generative model of question-answer (QA) pairs for mac...

Robust Domain Adaptation for Machine Reading Comprehension

Most domain adaptation methods for machine reading comprehension (MRC) u...

Generating Question-Answer Hierarchies

The process of knowledge acquisition can be viewed as a question-answer ...

Accelerating Real-Time Question Answering via Question Generation

Existing approaches to real-time question answering (RTQA) rely on learn...

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

Generating some appealing questions in open-domain conversations is an e...

Please sign up or login with your details

Forgot password? Click here to reset