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。

基于 Transformer 编码器的多准则中文分词简明模型

A Concise Model for Multi-Criteria Chinese Word Segmentation with  Transformer Encoder

Multi-criteria Chinese word segmentation is a promising but challenging task,
which exploits several different segmentation criteria and mines their common
underlying knowledge. In this paper, we propose a flexible multi-criteria
learning for Chinese word segmentation. Usually, a segmentation criterion could
be decomposed into multiple sub-criteria, which are shareable with other
segmentation criteria. The process of word segmentation is a routing among
these sub-criteria. From this perspective, we present Switch-LSTMs to segment
words, which consist of several long short-term memory neural networks (LSTM),
and a switcher to automatically switch the routing among these LSTMs. With
these auto-switched LSTMs, our model provides a more flexible solution for
multi-criteria CWS, which is also easy to transfer the learned knowledge to new
criteria. Experiments show that our model obtains significant improvements on
eight corpora with heterogeneous segmentation criteria, compared to the
previous method and single-criterion learning.

本文提出了一种灵活的多标准中文分词学习方法：Switch-LSTMs，它由几个长短时记忆神经网络 (LSTM) 和一个自动选择器组成，可以在多个子标准之间灵活切换，相比之前的方法和单一标准学习，该模型在八个具有不同标准的语料库上取得了显著的改进。