HACAN: a hierarchical answer-aware and context-aware network for question generation

doi:10.1007/s11704-023-2246-2

Front. Comput. Sci.

2024, Vol. 18

Issue (5) : 185321 https://doi.org/10.1007/s11704-023-2246-2

Artificial Intelligence

HACAN: a hierarchical answer-aware and context-aware network for question generation

Ruijun SUN, Hanqin TAO, Yanmin CHEN, Qi LIU(

)

Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, Hefei 230027, China

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Abstract

Question Generation (QG) is the task of generating questions according to the given contexts. Most of the existing methods are based on Recurrent Neural Networks (RNNs) for generating questions with passage-level input for providing more details, which seriously suffer from such problems as gradient vanishing and ineffective information utilization. In fact, reasonably extracting useful information from a given context is more in line with our actual needs during questioning especially in the education scenario. To that end, in this paper, we propose a novel Hierarchical Answer-Aware and Context-Aware Network (HACAN) to construct a high-quality passage representation and judge the balance between the sentences and the whole passage. Specifically, a Hierarchical Passage Encoder (HPE) is proposed to construct an answer-aware and context-aware passage representation, with a strategy of utilizing multi-hop reasoning. Then, we draw inspiration from the actual human questioning process and design a Hierarchical Passage-aware Decoder (HPD) which determines when to utilize the passage information. We conduct extensive experiments on the SQuAD dataset, where the results verify the effectiveness of our model in comparison with several baselines.

Keywords question generation natural language generation natural language processing sequence to sequence

Corresponding Author(s): Qi LIU

Just Accepted Date: 05 May 2023 Issue Date: 10 July 2023

Cite this article:

Ruijun SUN,Hanqin TAO,Yanmin CHEN, et al. HACAN: a hierarchical answer-aware and context-aware network for question generation[J]. Front. Comput. Sci., 2024, 18(5): 185321.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-2246-2
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I5/185321

Fig.1 Two examples of QG. The questions are generated by the given contexts and answers. The context could be a sentence or a passage and the answer is a sub-span of words from the context. The passage is from the SQuAD dataset

Fig.2 The structure of HACAN which contains HPE and HPD

Fig.3 The details of two types Multi-Hop Blocks

Tab.1 Experimental results on the SQuAD dataset

Fig.4 The effect of the hop count on the results

Fig.5 Examples generated by HACAN verify how HACAN constructs and exploits passage representation. The passage is from the SQuAD dataset

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