Meta Auxiliary Learning for Low-resource Spoken Language Understanding
Yingying Gao, Junlan Feng, Chao Deng, Shilei Zhang · Interspeech 2022 · 2022
Spoken language understanding (SLU) treats automatic speech recognition (ASR) and natural language understanding (NLU) as a unified task and usually suffers from data scarcity.We exploit an ASR and NLU joint training method based on meta auxiliary learning to improve the performance of low-resource SLU task by only taking advantage of abundant manual transcriptions of speech data.One obvious advantage of such method is that it provides a flexible framework to implement a lowresource SLU training task without requiring access to any further semantic annotations.In particular, a NLU model is taken as label generation network to predict intent and slot tags from texts; a multi-task network trains ASR task and SLU task synchronously from speech; and the predictions of label generation network are delivered to the multi-task network as semantic targets.The efficiency of the proposed algorithm is demonstrated with experiments on the public CATSLU dataset, which produces more suitable ASR hypotheses for the downstream NLU task.