Question Answering and Question Generation as Dual Tasks (1706.02027v2)

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 the parameters involved in these two models could be conventionally learned with back propagation. We conduct experiments on three datasets. Empirical results show that our training framework improves both QA and QG tasks. The improved QA model performs comparably with strong baseline approaches on all three datasets.

Question Answering and Question Generation as Dual Tasks

The paper presents a paper focused on the dual nature of question answering (QA) and question generation (QG) within natural language processing. The authors propose that these two tasks, when treated jointly, can enhance one another by leveraging their intrinsic connection. This is based on the understanding that QA and QG are reversely related, where the input-output dynamic for one task essentially mirrors the other.

Framework and Modeling

The proposed framework establishes QA and QG as dual tasks, utilizing their probabilistic correlation in training both models simultaneously. A sequence-to-sequence model underpins the QG task, aiming to generate questions from a given answer sentence, while a recurrent neural network is employed for the QA task, which evaluates the relevance of answers to given questions.

Their joint probabilistic modeling is reflected in the equation:

$P(q, a) = P(a) P(q|a) = P(q) P(a|q)$

This illustrates how the probabilities $P(q|a)$ and $P(a|q)$, associated with QG and QA respectively, can be constrained to improve the task outcomes.

Experimental Evidence

The empirical analysis was conducted across three datasets: MARCO, SQUAD, and WikiQA. The dual task framework demonstrated improvements in both QA and QG performance, with Dual QA showing higher Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), and Precision@1 (P@1) compared to baseline models. Dual QG also yielded higher BLEU-4 scores across all datasets, indicating more accurate question generation.

Implications and Speculation

The findings highlight the significance of leveraging task duality for enhancing NLP models. Theoretically, this dual framework could provide a refined understanding of how language constructs influence cognitive tasks such as QA and QG, pointing to deeper interdependencies than previously recognized. Practically, this joint model training could inform better design of applications, such as intelligent search engines and conversational agents.

Future developments in AI could explore broader applications of dual task training frameworks beyond QA and QG, potentially improving models where tasks exhibit similar intrinsic connections. The dual methodology might be expanded to cover multimodal tasks, integrating language with vision or audio processing. Furthermore, improving inferential processes alongside duality-based training for QA and QG could deliver more nuanced question-answer modeling, enhancing user engagement and satisfaction in AI-driven interfaces.

In conclusion, this paper contributes to advancing the prediction accuracy and generative capacity of QA and QG models while illustrating a promising direction for leveraging duality in diverse AI applications.