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| There has been plenty of research on Chinese polyphonic disambiguation (CPD) problems. However, badcases are always found in real-life products. To fix such bad cases without affecting system performance on known cases is a rigid demand. In this paper, continual learning is introduced to CPD problems, and Progressive Neural Networks (PNN) is used to learn new knowledge from bad cases without sacrificing system performance on old datasets. The experimental results show that the proposed method can repair the badcase without forgetting the feature of the original dataset. Compared with the traditional finetune method, the accuracy of the model on the old dataset decreases by nearly 20\%. Our method can ensure that the accuracy of the original dataset just decreases by about 0.3\% after learning the new feature data, and the time consumption is acceptable. Potential improvements like weight pruning are also discussed. |
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| Keywords:Chinese Polyphone Disambiguation, Online Learning,PNN, MLP, LSTM |
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