Self-attention networks achieve competitive results to BiLSTM-CRF on Chinese word segmentation, with BERT and word integration yielding the best reported performance on six heterogeneous domain benchmarks.
Neural Word Segmentation with Rich Pretraining
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abstract
Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.
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Investigating Self-Attention Network for Chinese Word Segmentation
Self-attention networks achieve competitive results to BiLSTM-CRF on Chinese word segmentation, with BERT and word integration yielding the best reported performance on six heterogeneous domain benchmarks.