Transfer Learning Methods for Spoken Language Understanding.
ICMI(2019)
摘要
In this paper, we present a series of methods to improve the performance of spoken language understanding in the 1st Chinese Audio-Textual Spoken Language Understanding Challenge (CATSLU 2019) which is aimed to improve the robustness for automatic speech recognition (ASR) errors and to solve the problem of not enough labeled data in new domains. We combine word information and char information to improve the performance of the semantic parser. We also use some transfer learning methods like correlation alignments to improve the robustness of the spoken language understanding system. Then we merge the rule method and the neural network method to raise system output performance. In video and weather domains with few training data, we use both the transfer learning model trained on multi-domain data and the rule-based approach. Our approaches achieve F1 scores of 86.83%, 92.84%, 94.16%, and 93.04% on the test sets of map, music, video and weather domains.
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关键词
Spoken language understanding, ASR-error adaptation, Domain adaptation
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