A Dynamic Attention and Multi-Strategy-Matching Neural Network Based on Bert for Chinese Rice-Related Answer Selection

Abstract

To allow the intelligent detection of correct answers in the rice-related question-and-answer (Q&A) communities of the “China Agricultural Technology Extension Information Platform,” we propose an answer selection model with dynamic attention and multi-strategy matching (DAMM). According to the characteristics of the rice-related dataset, the twelve-layer Chinese Bert pre-training model was employed to vectorize the text data and was compared with Word2vec, GloVe, and TF-IDF (Term Frequency–Inverse Document Frequency) methods. It was concluded that Bert could effectively solve the agricultural text’s high dimensionality and sparsity problems. As well as the problem of polysemy having different meanings in different contexts, dynamic attention with two different filtering strategies was used in the attention layer to effectively remove the sentence’s noise. The sentence representation of question-and-answer sentences was obtained. Secondly, two matching strategies (Full matching and Attentive matching) were introduced in the matching layer to complete the interaction between sentence vectors. Thirdly, a bi-directional gated recurrent unit (BiGRU) network spliced the sentence vectors obtained from the matching layer. Finally, a classifier was employed to calculate the similarity of splicing vectors, and the semantic correlation between question-and-answer sentences was acquired. The experimental results showed that DAMM had the best performance in the rice-related answer selection dataset compared with the other six answer selection models, of which MAP (Mean Average Precision) and MRR (Mean Reciprocal Rank) of DAMM gained 85.7% and 88.9%, respectively. Compared with the other six kinds of answer selection models, we present a new state-of-the-art method with the rice-related answer selection dataset.