Adversarial Transfer Learning for Punctuation Restoration
TABLE VI: Transferring knowledge from a POS tagging task to a punctuation predicting task. The results of punctuation predicting models in terms of P(%) ,R(%) , F1(%) on test sets of IWSLT2011 datasets.
['Test', 'Model', '[ITALIC] COMMA  [ITALIC] P', '[ITALIC] COMMA  [ITALIC] R', '[ITALIC] COMMA  [ITALIC] F1', '[ITALIC] PERIOD  [ITALIC] P', '[ITALIC] PERIOD  [ITALIC] R', '[ITALIC] PERIOD  [ITALIC] F1', '[ITALIC] QUESTION  [ITALIC] P', '[ITALIC] QUESTION  [ITALIC] R', '[ITALIC] QUESTION  [ITALIC] F1', '[ITALIC] Overall  [ITALIC] P', '[ITALIC] Overall  [ITALIC] R', '[ITALIC] Overall  [ITALIC] F1']
The results show that with the help of the POS tagging task via multi-task learning, the punctuation restoring task obtains performance improvement both on Ref. and ASR test sets. The results also demonstrate that when the multi-task model with an additional task classifier, the performance of the punctuation predicting models improve moderately on two test sets. However, the punctuation predicting models achieve further obvious improvements when the multi-task model with an extra adversarial task discriminator on Ref. and ASR test sets, respectively.