ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information

This repository contains code, model, dataset for ChineseBERT at ACL2021.

ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li

Guide

Section	Description
Introduction	Introduction to ChineseBERT
Download	Download links for ChineseBERT
Quick tour	Learn how to quickly load models
Experiment	Experiment results on different Chinese NLP datasets
Citation	Citation
Contact	How to contact us

Introduction

We propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese characters into language model pretraining.

First, for each Chinese character, we get three kind of embedding.

Char Embedding: the same as origin BERT token embedding.
Glyph Embedding: capture visual features based on different fonts of a Chinese character.
Pinyin Embedding: capture phonetic feature from the pinyin sequence ot a Chinese Character.

Then, char embedding, glyph embedding and pinyin embedding are first concatenated, and mapped to a D-dimensional embedding through a fully connected layer to form the fusion embedding.
Finally, the fusion embedding is added with the position embedding, which is fed as input to the BERT model.
The following image shows an overview architecture of ChineseBERT model.

ChineseBERT leverages the glyph and pinyin information of Chinese characters to enhance the model's ability of capturing context semantics from surface character forms and disambiguating polyphonic characters in Chinese.

Download

We provide pre-trained ChineseBERT models in Pytorch version and followed huggingFace model format.

ChineseBERT-base：12-layer, 768-hidden, 12-heads, 147M parameters
ChineseBERT-large: 24-layer, 1024-hidden, 16-heads, 374M parameters

Our model can be downloaded here:

Model	Model Hub	Size
`ChineseBERT-base`	Pytorch	564M
`ChineseBERT-large`	Pytorch	1.4G

Note: The model hub contains model, fonts and pinyin config files.

Quick tour

We train our model with Huggingface, so the model can be easily loaded.
Download ChineseBERT model and save at [CHINESEBERT_PATH].
Here is a quick tour to load our model.

>>> from models.modeling_glycebert import GlyceBertForMaskedLM

>>> chinese_bert = GlyceBertForMaskedLM.from_pretrained([CHINESEBERT_PATH])
>>> print(chinese_bert)

The complete example can be find here: Masked word completion with ChineseBERT

Another example to get representation of a sentence:

>>> from datasets.bert_dataset import BertDataset
>>> from models.modeling_glycebert import GlyceBertModel

>>> tokenizer = BertDataset([CHINESEBERT_PATH])
>>> chinese_bert = GlyceBertModel.from_pretrained([CHINESEBERT_PATH])
>>> sentence = '我喜欢猫'

>>> input_ids, pinyin_ids = tokenizer.tokenize_sentence(sentence)
>>> length = input_ids.shape[0]
>>> input_ids = input_ids.view(1, length)
>>> pinyin_ids = pinyin_ids.view(1, length, 8)
>>> output_hidden = chinese_bert.forward(input_ids, pinyin_ids)[0]
>>> print(output_hidden)
tensor([[[ 0.0287, -0.0126,  0.0389,  ...,  0.0228, -0.0677, -0.1519],
         [ 0.0144, -0.2494, -0.1853,  ...,  0.0673,  0.0424, -0.1074],
         [ 0.0839, -0.2989, -0.2421,  ...,  0.0454, -0.1474, -0.1736],
         [-0.0499, -0.2983, -0.1604,  ..., -0.0550, -0.1863,  0.0226],
         [ 0.1428, -0.0682, -0.1310,  ..., -0.1126,  0.0440, -0.1782],
         [ 0.0287, -0.0126,  0.0389,  ...,  0.0228, -0.0677, -0.1519]]],
       grad_fn=)

The complete code can be find HERE