Knowledge Augmented BERT Mutual Network in Multi-turn Spoken Dialogues

• URL: http://arxiv.org/abs/2202.11299v1
• Date: Wed, 23 Feb 2022 04:03:35 GMT
• Title: Knowledge Augmented BERT Mutual Network in Multi-turn Spoken Dialogues
• Authors: Ting-Wei Wu and Biing-Hwang Juang
• Abstract summary: We propose to equip a BERT-based joint model with a knowledge attention module to mutually leverage dialogue contexts between two SLU tasks. A gating mechanism is further utilized to filter out irrelevant knowledge triples and to circumvent distracting comprehension. Experimental results in two complicated multi-turn dialogue datasets have demonstrate by mutually modeling two SLU tasks with filtered knowledge and dialogue contexts.
• Score: 6.4144180888492075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern spoken language understanding (SLU) systems rely on sophisticated semantic notions revealed in single utterances to detect intents and slots. However, they lack the capability of modeling multi-turn dynamics within a dialogue particularly in long-term slot contexts. Without external knowledge, depending on limited linguistic legitimacy within a word sequence may overlook deep semantic information across dialogue turns. In this paper, we propose to equip a BERT-based joint model with a knowledge attention module to mutually leverage dialogue contexts between two SLU tasks. A gating mechanism is further utilized to filter out irrelevant knowledge triples and to circumvent distracting comprehension. Experimental results in two complicated multi-turn dialogue datasets have demonstrate by mutually modeling two SLU tasks with filtered knowledge and dialogue contexts, our approach has considerable improvements compared with several competitive baselines.

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