NLPIR SEMINAR Y2019#8
In the new semester, our Lab, Web Search Mining and Security Lab, plans to hold an academic seminar every Monday, and each time a keynote speaker will share understanding of papers on his/her related research with you.
This week’s seminar is organized as follows:
- The seminar time is 1.pm, Mon, at Zhongguancun Technology Park ,Building 5, 1306.
- The lecturer is
Li Shen , the paper’s title is Glyce: Glyph-vectors for Chinese Character Representations.
- The seminar will be hosted by Zhaoyang Wang.
- Attachment is the paper of this seminar, please download in advance.
Everyone interested in this topic is welcomed to join us. the following is the abstract for this week’s paper.
Glyce: Glyph-vectors for Chinese Character Representations
Yuxian Meng, Wei
Wu, Qinghong Han, Muyu Li, Xiaoya Li, Jie Mei, Ping Nie, Xiaofei Sun and Jiwei
It is intuitive that NLP tasks for
logographic languages like Chinese should benefit from the use of the glyph
information in those languages. However, due to the lack of rich pictographic
evidence in glyphs and the weak generalization ability of standard computer
vision models on character data, an effective way to utilize the glyph
information remains to be found.
In this paper, we address this gap by
presenting the Glyce, the glyph-vectors for Chinese character representations.
We make three major innovations: (1) We use historical Chinese scripts (e.g.,
bronzeware script, seal script, traditional Chinese, etc) to enrich the
pictographic evidence in characters; (2) We design CNN structures tailored to
Chinese character image processing; and (3) We use image-classification as an
auxiliary task in a multi-task learning setup to increase the model’s ability
For the first time, we show that
glyph-based models are able to consistently outperform word/char ID-based
models in a wide range of Chinese NLP tasks. Using Glyce, we are able to
achieve the state-of-the-art performances on 13 (almost all) Chinese NLP tasks,
including (1) character-Level language modeling, (2) word-Level language
modeling, (3) Chinese word segmentation, (4) name entity recognition, (5)
part-of-speech tagging, (6) dependency parsing, (7) semantic role labeling, (8)
sentence semantic similarity, (9) sentence intention identification, (10)
Chinese-English machine translation, (11) sentiment analysis, (12) document
classification and (13) discourse parsing.