基于BERT-BIGRU-CRF *的邻接对预测核函数研究

Xin Chen, Zhanzhi Qiu
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引用次数: 2

摘要

针对在对话过程中如何根据不同说话人的轮次预测先行词和后语属于邻接对的核心功能,从而实现对对话过程的整体控制,提出了基于BERT-BIGRU-CRF的核心功能预测模型。该模型的输入是对话者的不同回合,该模型首先使用BERT得到矢量化的句子,然后将其输入到BIGRU中进行双向编码,最后使用CRF形成其核心函数的标记,并通过S、R、Q、A和DO相邻完成核心函数的输出。实验表明,该预测方法比之前的顺序标注模型具有更好的性能,其准确率和F1分数均有显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Core Function of Adjacency Pairs Prediction Based on BERT-BIGRU-CRF∗
Based on how to predict the core functions of the antecedent and the afterword belongs to the adjacency pairs according to different speaker's turn in the process of dialogue, So as to achieve overall control of the dialogue process, The core function prediction model based on BERT-BIGRU-CRF is put forward. The inputs of the model are the different turns of the conversationalist, Firstly, the model used BERT get sentence of vectorization, then it is input into BIGRU to make the two directional encoding, Finally, CRF is used to form the mark of its core function and the output of core function by S, R, Q, A and DO adjacent is completed. Experiments show that this prediction method has better performance than the previous sequential labeling model and its accuracy and F1 score are significantly improved.
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