State-of-the-art Chinese Word Segmentation with Bi-LSTMs

EMNLP 2018 Ji MaKuzman GanchevDavid Weiss

A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. Surprisingly, we find that a bidirectional LSTM model, when combined with standard deep learning techniques and best practices, can achieve better accuracy on many of the popular datasets as compared to models based on more complex neural-network architectures... (read more)

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