Aspect-Level Sentiment Difference Feature Interaction Matching Model Based on Multi-round Decision Mechanism

Abstract

Sentence matching is a key problem in natural language understanding, so the research on sentence matching can be applied to a large number of known natural language processing tasks, such as information retrieval, automatic question and answer, machine translation, dialogue system, paraphrase identification etc. In a series of natural language processing tasks, we need to rely on the participation and collaboration of the sentence matching model. The performance of the sentence matching model can greatly affect the final performance of these natural language processing tasks. We propose the Al-SFIM model, which improves the matching model from the perspective of word interaction. First, we propose sentiment attention mechanism based on the distribution of aspect-level sentiment difference to improve the interaction between cross-sentence words, and use the sentiment space position perception vector to improve the interaction between intra-sentence words, so that the model has the ability to perceive the subjective sentiment difference in the process of intra-sentence word interaction and cross-sentence word interaction. Then, we introduce a multi-round decision mechanism based on the accumulation of memory state, which iteratively updates the working memory state to make matching decisions in multiple rounds, so that the model can better understand the semantic of complex sentence. Experiment results show that the AL-SFIM model has made progress in sentence matching and has better matching performance for complex, long and incomprehensible sentences.