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A Residual LSTM and Seq2Seq Neural Network Based on GPT for Chinese Rice-Related Question and Answer System

文献类型： 外文期刊

作者： Wang, Haoriqin 1 ; Wu, Huarui 2 ; Zhu, Huaji 2 ; Miao, Yisheng 2 ; Wang, Qinghu 1 ; Qiao, Shicheng 1 ; Zhao, Haiyan 1 ; Chen, Cheng 2 ; Zhang, Jingjian 6 ;

作者机构： 1.Inner Mongolia Minzu Univ, Coll Comp Sci & Technol, Tongliao 028043, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100097, Peoples R China

3.Minist Agr & Rural Affairs Peoples Republ China, Agr Key Lab Digital Village, Beijing 100097, Peoples R China

4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

5.Shenyang Agr Univ, Sch Informat & Elect Engn, Shenyang 110866, Peoples R China

6.CangZhou Acad Agr & Forestry Sci, Cangzhou 061001, Peoples R China

关键词： rice-related question and answer; Residual Long Short-Term Memory; question-and-answer communities; seq2seq

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）

ISSN：

年卷期： 2022 年 12 卷 6 期

页码：

收录情况： SCI

摘要： Rice has a wide planting area as one of the essential food crops in China. The problem of diseases and pests in rice production has always been one of the main factors affecting its quality and yield. It is essential to provide treatment methods and means for rice diseases and pests quickly and accurately in the production process. Therefore, we used the rice question-and-answer (Q&A) community as an example. This paper aimed at the critical technical problems faced by the agricultural Q&A community: the accuracy of the existing agricultural Q&A model is low, which is challenging to meet users' requirements to obtain answers in real-time in the production process. A network based on Attention-ResLSTM-seq2seq was used to realize the construction of the rice question and answer model. Firstly, the text presentation of rice question-and-answer pairs was obtained using the GPT pre-training model based on a 12-layer transformer. Then, ResLSTM(Residual Long Short-Term Memory) was used to extract text features in the encoder and decoder, and the output project matrix and output gate of LSTM were used to control the spatial information flow. When the network contacts the optimal state, the network only retains the constant mapping value of the input vector, which effectually reduces the network parameters and increases the network performance. Next, the attention mechanism was connected between the encoder and the decoder, which can effectually strengthen the weight of the keyword feature information of the question. The results showed that the BLEU and ROUGE of the Attention-ResLSTM-Seq2seq model reached the highest scores, 35.3% and 37.8%, compared with the other six rice-related generative question answering models.

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