Data Annealing for Informal Language Understanding Tasks
Table 1: Results on NER, POS tagging and chunking task. * means the difference between DA BERTLARGE and state-of-the-art results. ** means the state-of-the-art for these three tasks are achieved by different models. Yang et al. (2019), Gui et al. (2018) and Yang et al. (2017) proposed the state-of-the-art model on NER, POS tagging and chunking respectively.
['model', 'NER  [ITALIC] P', 'NER  [ITALIC] R', 'NER  [ITALIC] F1', 'POS  [ITALIC] A', 'Chunking  [ITALIC] P', 'Chunking  [ITALIC] R', 'Chunking  [ITALIC] F1']
Vanilla means the model is trained without transfer learning, in other words, the model does not utilize the source data. DA means the model is implemented with data annealing procedure. All the numbers in the tables are the average result of three runs. It is worth noting that state-of-the-art results on these three tasks are achieved by different models. While our proposed data annealing algorithm is applied to the same simple BERT or LSTM structure without fancy decoration.