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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (5): 44-53    DOI: 10.11925/infotech.2096-3467.2021.0857
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Question Generation Based on Sememe Knowledge and Bidirectional Attention Flow
Duan Jianyong1,2(),Xu Lishan1,Liu Jie1,2,Li Xin1,2,Zhang Jiaming1,Wang Hao1,2
1School of Information, North China University of Technology, Beijing 100144, China
2CNONIX National Standard Application and Promotion Laboratory, Beijing 100144, China
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Abstract  

[Objective] This paper proposes a question generation model based on sememe knowledge and bidirectional attention flow, aiming to improve the semantics of the questions. [Methods] We developed two strategies to enhance semantics: (I) By integrating the external knowledge of sememe in the embedding layer, we captured the semantic knowledge with a smaller granularity than word vectors, and then enhanced the semantic features of the text itself. In addition, we obtained an expanded sememe knowledge base that is more in line with the semantics of the contextual text through the cosine similarity algorithm. It helped us filter out the sememes creating semantic noise in the original knowledge base, and recommended semantically compliant sememe sets for words labeled with non-semantic origins. (II) We enhanced the semantic representation between texts and answers by incorporating a bidirectional attention flow after the encoding layer. [Results] We evaluated our model with the SQuAD1.1 dataset, and the Bleu_1, Bleu_2, Bleu_3, and Blue_4 reached 46.70%, 31.07%, 22.90%, and 17.48%, respectively. The proposed model outperformed the baseline models. [Limitations] With the bidirectional attention flow, the model needs to extract features of paragraph texts and questions, which demands double memory and time to train the model. [Conclusions] Sememe knowledge and bidirectional attention flow could help the proposed model generate higher-quality questions more in line with human language habits.

Key wordsQuestion Generation      Sememe Knowledge      Cosine Similarity      Bidirectional Attention Flow     
Received: 19 August 2021      Published: 01 March 2022
ZTFLH:  TP391  
Fund:National Natural Science Foundation of China(61972003);Humanities and Social Science Foundation of the Ministry of Education(21YJA740052)
Corresponding Authors: Duan Jianyong,ORCID: 0000-0002-2244-3764     E-mail: duanjy@ncut.edu.cn

Cite this article:

Duan Jianyong, Xu Lishan, Liu Jie, Li Xin, Zhang Jiaming, Wang Hao. Question Generation Based on Sememe Knowledge and Bidirectional Attention Flow. Data Analysis and Knowledge Discovery, 2022, 6(5): 44-53.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0857     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I5/44

Encoder-Decoder Model
Question Generation Model Based on the Sememe Knowledge and Bidirectional Attention Flow
Example of the Semantic Tree of the Word “cardinal” in HowNet
硬件 参数
处理器 Intel(R)
操作系统 Linux
CPU核数 8核
GPU块数 4块
GPU型号 GeForce GTX 1080 Ti
GPU大小 12GB
Experimental Hardware Configuration Parameters
参数
Epoch 50
Max_len 400
学习率 0.2
Batch_size 18
Dropout 0.3
Beam_size 10
Optimal Parameter Settings for Experiments
模型 Bleu_1 Bleu_2 Bleu_3 Bleu_4
Seq2Seq 31.34 13.79 7.36 4.26
NQG++ 43.09 25.96 17.50 12.28
Ass2s - - - 16.20
s2s-map-gsa 45.69 30.25 22.16 16.85
Our model 46.70 31.07 22.90 17.48
Results of Question Generation Model Based on Sememe Knowledge and Bidirectional Attention Flow (%)
集合中义原的个数 Bleu_1 Bleu_2 Bleu_3 Bleu_4
1 46.21 30.60 22.40 17.08
2 46.70 31.07 22.90 17.48
3 46.64 30.89 22.71 17.38
4 46.37 30.65 22.47 17.05
The Influence of the Number of Sememe in the Collection on Performance of the Model (%)
义原作用域 Bleu_1 Bleu_2 Bleu_3 Bleu_4
仅文本 46.62 30.96 22.75 17.33
仅答案 46.42 30.77 22.53 17.31
文本和答案 46.70 31.07 22.90 17.48
The Influence of Sememe Scope on the Model (%)
模型 Bleu_1 Bleu_2 Bleu_3 Bleu_4
s2s-map-gsa 45.69 30.25 22.16 16.85
+ prediction_sememes 46.17 30.64 22.53 17.20
+ FClayer 46.11 30.41 22.26 16.97
Our Model 46.70 31.07 22.90 17.48
Sememe Knowledge and Bidirectional Attention Flow Mechanism Respectively Affect the Model’s Results (%)
Sample Analysis of Problems Generated by Baseline Model
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