Leveraging Intent Entity Enhancement for Task-Oriented Dialogue

Task-Oriented Dialogue commonly utilizes external knowledge bases to respond user utterances to complete specific tasks. Existing models use whole dialogue history record directly, which contain massive irrelevant noise entities, decreasing accuracy in knowledge retrieval. Thus, how to avoid noise entities and effectively identify relevant entities representing user intents within long dialogue history record remains as a crucial challenge. This paper proposes a new model named IEM consisted of a knowledge selector to retrieve relevant knowledge and a response generator to generate system responses. In addition, this paper introduces an intent retrieval mechanism that search for intent entities from dialogue history and generate intent prompts for task-oriented dialogue. Experiments on three publicly available datasets show that our IEM is superior to existing state-of-the-art baseline methods, verifying its effectiveness in task-oriented dialogue. Moreover, the results demonstrate the promising adaptability of intent retrieval mechanism in incorporating into large language models.