Recent Advances in End-to-End Spoken Language Understanding
Table 2: NER results on the test dataset in terms of Precision (P,%), Recall (R,%) and F-measure (F, %) for SI and SAT models.
['[BOLD] Model training', '[BOLD] SI  [BOLD] P', '[BOLD] SI  [BOLD] R', '[BOLD] SI  [BOLD] F', '[BOLD] SAT  [BOLD] P', '[BOLD] SAT  [BOLD] R', '[BOLD] SAT  [BOLD] F']
Performance of NER was evaluated in terms of precision, recall, and F-measure. We can see, that pretraining with ASRF task does not lead to significant improvement in performance. When the NER⋆ is added to the training chain, it improves all the evaluation measures. In particular, F-measure is increased by 1.9% absolute. For each training chain, we trained a corresponding chain with speaker adaptation. For all training chains, SAT models outperform SI models. The best result with SAT (F-measure 71.8%) outperforms the best SI result by 1.1% absolute.