Multi-Criteria Chinese Word Segmentation (MCCWS) aims at finding word
boundaries in a Chinese sentence composed of continuous characters while
multiple segmentation criteria exist. The unified framework has been widely
used in MCCWS and shows its effectiveness. Besides, the pre-trained BERT
language model has been also introduced into the MCCWS task in a multi-task
learning framework. In this paper, we combine the superiority of the unified
framework and pretrained language model, and propose a unified MCCWS model
based on BERT. Moreover, we augment the unified BERT-based MCCWS model with the
bigram features and an auxiliary criterion classification task. Experiments on
eight datasets with diverse criteria demonstrate that our methods could achieve
new state-of-the-art results for MCCWS.

本文利用预训练 Bert 模型和 bigram 特征，提出了一个新的基于 Bert 的统一的 MCCWS 模型并加入了一个辅助分类任务，在 8 个具有不同标准的数据集上进行实验，并取得了新的最优结果。

使用 BERT 统一多准则中文分词

Unified Multi-Criteria Chinese Word Segmentation with BERT

Multi-criteria Chinese word segmentation (MCCWS) aims to exploit the
relations among the multiple heterogeneous segmentation criteria and further
improve the performance of each single criterion. Previous work usually regards
MCCWS as different tasks, which are learned together under the multi-task
learning framework. In this paper, we propose a concise but effective unified
model for MCCWS, which is fully-shared for all the criteria. By leveraging the
powerful ability of the Transformer encoder, the proposed unified model can
segment Chinese text according to a unique criterion-token indicating the
output criterion. Besides, the proposed unified model can segment both
simplified and traditional Chinese and has an excellent transfer capability.
Experiments on eight datasets with different criteria show that our model
outperforms our single-criterion baseline model and other multi-criteria
models. Source codes of this paper are available on Github
this https URL

提出了一种基于 Transformer 编码器的多准则中文词分割 (MCCWS) 统一模型，该模型可对简体和繁体中文进行分割，并具有出色的迁移能力。在八个具有不同准则的数据集上进行实验，结果表明我们的模型优于单准则基线模型和其他多准则模型。