Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portability
Table 5: Results on the PORTMEDIA test data for domain portability
['Training chain', 'CER', 'CVER']
Last experiments address the portability issue for SLU systems. The PORTMEDIA corpus was produced in order to investigate two axes of portability: domain and language In this work we only focus on domain portability by using the PORTMEDIA corpus previously described, that is actually only a sub part of the PORTMEDIA data that also contain an Italian version of MEDIA. ASR∙SFM∙SFP shows that we can reach 25.2% of CER when the SFP model is trained from a system dedicated to MEDIA (ASR∙SFM), that is better than starting directly from the ASR model or from scratch. At the end, to reach the best result we can, the approach consists on applying the same curriculum-based transfer learning used in the previous experiments when targeting MEDIA. In order to reduce computational costs to develop an SLU dedicated to a new task, it seems that the best way is to save the ASR∙NER model and to start the new training chain from it. ASR∙NER seems to be a very relevant starting point to tackle slot filling tasks. Notice that around 4 weeks of computation are needed to train the ASR∙NER model on two Titan X (Pascal) GPU cards, while only a few days are need for the SFP+M(⋆)∙SFP(⋆) part.