Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability
Table 1: Performance of end-to-end SLU system in greedy decoding on the MEDIA test
['Training chain', 'CER', 'CVER']
First experiments target the slot filling task in the MEDIA domain. CER is a metrics similar to the word error rate metrics but applied on concepts. CVER is very close to CER but evaluate concept/value pairs instead of evaluating only concepts. Initializing weights pretrained on the ASR task (ASR) before training SFM very strongly reduces both CER and CVER, since the system ASR∙SFM reaches 23.7% of CER and 30.3% of CVER. Exploiting the merge of the MEDIA and PORT-MEDIA corpora as an intermediate transfer learning task (SFP+M) between ASR and SFM training processes allows us to reduce again both CER and CVER. Last, the deep neural model trained by following the complete curriculum-based transfer learning proposed in this paper reaches 21.6% in greedy mode. This complete chain training integrates the name entity recognition tasks, called NER, between ASR and SFP+M training steps.