A Context-Aware Hierarchical BERT Fusion Network for Multi-turn Dialog Act Detection
This work addresses the challenge of understanding user intents in interactive dialog systems, which is crucial for improving spoken language understanding, though it is incremental in nature.
The paper tackled the problem of multi-turn dialog act detection by proposing a context-aware hierarchical BERT fusion network, achieving new state-of-the-art performances on two complex datasets with significant improvements over previous methods.
The success of interactive dialog systems is usually associated with the quality of the spoken language understanding (SLU) task, which mainly identifies the corresponding dialog acts and slot values in each turn. By treating utterances in isolation, most SLU systems often overlook the semantic context in which a dialog act is expected. The act dependency between turns is non-trivial and yet critical to the identification of the correct semantic representations. Previous works with limited context awareness have exposed the inadequacy of dealing with complexity in multiproned user intents, which are subject to spontaneous change during turn transitions. In this work, we propose to enhance SLU in multi-turn dialogs, employing a context-aware hierarchical BERT fusion Network (CaBERT-SLU) to not only discern context information within a dialog but also jointly identify multiple dialog acts and slots in each utterance. Experimental results show that our approach reaches new state-of-the-art (SOTA) performances in two complicated multi-turn dialogue datasets with considerable improvements compared with previous methods, which only consider single utterances for multiple intents and slot filling.