Question Answering and Question Generation as Dual Tasks
Duyu Tang, Nan Duan, Tao Qin, Zhao Yan, Ming Zhou

TL;DR
This paper introduces a dual-task training framework for joint question answering and question generation, leveraging their intrinsic connection to mutually improve both tasks through probabilistic correlation.
Contribution
The paper proposes a novel dual-task training approach that explicitly models the probabilistic relationship between QA and QG, enhancing both models' performance.
Findings
Improved performance of QA and QG models on three datasets.
Joint training outperforms separate models and baseline approaches.
Framework is fully differentiable and trainable via backpropagation.
Abstract
We study the problem of joint question answering (QA) and question generation (QG) in this paper. Our intuition is that QA and QG have intrinsic connections and these two tasks could improve each other. On one side, the QA model judges whether the generated question of a QG model is relevant to the answer. On the other side, the QG model provides the probability of generating a question given the answer, which is a useful evidence that in turn facilitates QA. In this paper we regard QA and QG as dual tasks. We propose a training framework that trains the models of QA and QG simultaneously, and explicitly leverages their probabilistic correlation to guide the training process of both models. We implement a QG model based on sequence-to-sequence learning, and a QA model based on recurrent neural network. As all the components of the QA and QG models are differentiable, all…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
