Rapidly developed neural models have achieved competitive performance in Chinese word segmentation (CWS) as their traditional counterparts. However, most of methods encounter the computational inefficiency especially for long sentences because of the increasing model complexity and slower decoders. This paper presents a simple neural segmenter which directly labels the gap existence between adjacent characters to alleviate the existing drawback. Our segmenter is fully end-to-end and capable of performing segmentation very fast. We also show a performance difference with different tag sets. The experiments show that our segmenter can provide comparable performance with state-of-the-art.
翻译:快速开发的神经模型在中国的单词分割(CWS)中取得了具有竞争力的成绩,但大多数方法都遇到了计算效率低下的情况,特别是由于模型复杂程度的提高和变慢的解析器,特别是长的句子。本文提出了一个简单的神经分离器,直接标出相邻字符之间存在的差距,以缓解现有的缺陷。我们的分解器是完全端端对端的,能够非常迅速地进行分解。我们还展示了不同标签组的性能差异。实验显示,我们的分解器可以提供最先进的类似性能。