Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding
This addresses a specific issue in Chinese SLU for natural language processing applications, but it is incremental as it builds on existing methods by adding word-level features.
The paper tackles the problem of Chinese spoken language understanding (SLU) by injecting word information to improve intent detection and slot filling, achieving state-of-the-art performance on two datasets.
In this paper, we improve Chinese spoken language understanding (SLU) by injecting word information. Previous studies on Chinese SLU do not consider the word information, failing to detect word boundaries that are beneficial for intent detection and slot filling. To address this issue, we propose a multi-level word adapter to inject word information for Chinese SLU, which consists of (1) sentence-level word adapter, which directly fuses the sentence representations of the word information and character information to perform intent detection and (2) character-level word adapter, which is applied at each character for selectively controlling weights on word information as well as character information. Experimental results on two Chinese SLU datasets show that our model can capture useful word information and achieve state-of-the-art performance.