Learning to select data for transfer learning with Bayesian Optimization
Table 4: Accuracy scores for cross-model transfer of learned data selection weights for part-of-speech tagging from Structured Perceptron (Pproxy) to Bi-LSTM tagger (B) (Plank et al., 2016) on the SANCL 2012 shared task dataset (Petrov and McDonald, 2012). Data selection weights are learned using model MS; Bi-LSTM tagger (B) is then trained using the learned weights. Better than baselines: underlined.
['[EMPTY]', '[BOLD] Target domains  [BOLD] Answers', '[BOLD] Target domains  [BOLD] Answers', '[BOLD] Target domains  [BOLD] Emails', '[BOLD] Target domains  [BOLD] Emails', '[BOLD] Target domains  [BOLD] Newsgroups', '[BOLD] Target domains  [BOLD] Newsgroups', '[BOLD] Target domains  [BOLD] Reviews', '[BOLD] Target domains  [BOLD] Reviews', '[BOLD] Target domains  [BOLD] Weblogs', '[BOLD] Target domains  [BOLD] Weblogs', '[BOLD] Target domains  [BOLD] WSJ', '[BOLD] Target domains  [BOLD] WSJ']
In addition, we are interested how well the metric learned for one target domain transfers to other settings. We first investigate its ability to transfer to another model. For this, we employ the data selection features learned using the Structured Perceptron model for POS tagging and use them to select data for the Bi-LSTM tagger. In particular, transferred diversity significantly outperforms its in-model equivalent. This is encouraging, as it allows to learn a data selection metric using less complex models.