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Erschienen in:
Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network

verfasst von : Jingjing Xu, Shuming Ma, Yi Zhang, Bingzhen Wei, Xiaoyan Cai, Xu Sun

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Recent studies have shown effectiveness in using neural networks for Chinese word segmentation. However, these models rely on large-scale data and are less effective for low-resource datasets because of insufficient training data. We propose a transfer learning method to improve low-resource word segmentation by leveraging high-resource corpora. First, we train a teacher model on high-resource corpora and then use the learned knowledge to initialize a student model. Second, a weighted data similarity method is proposed to train the student model on low-resource data. Experiment results show that our work significantly improves the performance on low-resource datasets: 2.3% and 1.5% F-score on PKU and CTB datasets. Furthermore, this paper achieves state-of-the-art results: 96.1%, and 96.2% F-score on PKU and CTB datasets.

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Vorheriges Kapitel Improved Compare-Aggregate Model for Chinese Document-Based Question Answering
Nächstes Kapitel AHNN: An Attention-Based Hybrid Neural Network for Sentence Modeling
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Metadaten
Titel
Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network
verfasst von
Jingjing Xu
Shuming Ma
Yi Zhang
Bingzhen Wei
Xiaoyan Cai
Xu Sun
Copyright-Jahr
2018
Buch
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-319-73617-4

Electronic ISBN: 978-3-319-73618-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-73618-1_62