Spoken Language Understanding with Sememe Knowledge as Domain Knowledge

Spoken language understanding (SLU) is a key procedure in task-oriented dialogue systems, its performance has been improved a lot due to deep neural network with pre-trained textual features. However, data sparsity and ASR error usually influence the model performance. Previous studies showed that pre-defined rules and domain knowledge such as lexicon features seems to be helpful for solving these issues. However, those methods are not flexible. In this study, we propose a new domain knowledge, ontology based sememe knowledge, and apply it in SLU task via a weighted sum network. To do so, we construct a sememe knowledge base by identifying slots' meanings and extracting the corresponding sememes from HowNet. We extract sememe sets for characters in given utterance and use them as domain knowledge in SLU task by means of the weighted sum network. Due to the weighted combinations of the sememe sets can extend words' meanings, the proposed method can help the model to flexibly match a sparse word to a specific slot. Evaluation on a Mandarin corpus showed that the proposed approach achieved better performance comparing to a leading method, and it also showed the robustness to ASR error.