# Span Labeling Approach for Vietnamese and Chinese Word Segmentation

arXiv: https://arxiv.org/abs/2110.00156 · HTML: https://mlsanity.com/paper/2110.00156/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [A Minimal Span-Based Neural Constituency Parser](https://mlsanity.com/paper/1705.03919/) (2017)
- [Fast and Accurate Neural CRF Constituency Parsing](https://mlsanity.com/paper/2008.03736/) (2020)
- [ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations](https://mlsanity.com/paper/1911.00720/) (2019)
- [A Fast and Accurate Vietnamese Word Segmenter](https://mlsanity.com/paper/1709.06307/) (2017)
- [State-of-the-art Chinese Word Segmentation with Bi-LSTMs](https://mlsanity.com/paper/1808.06511/) (2018)
- [Adversarial Multi-Criteria Learning for Chinese Word Segmentation](https://mlsanity.com/paper/1704.07556/) (2017)
- [From Word Segmentation to POS Tagging for Vietnamese](https://mlsanity.com/paper/1711.04951/) (2017)
- [Switch-LSTMs for Multi-Criteria Chinese Word Segmentation](https://mlsanity.com/paper/1812.08033/) (2018)
- [A neural joint model for Vietnamese word segmentation, POS tagging and dependency parsing](https://mlsanity.com/paper/1812.11459/) (2018)
- [Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation](https://mlsanity.com/paper/1711.04411/) (2017)
- [Vietnamese Word Segmentation with SVM: Ambiguity Reduction and Suffix Capture](https://mlsanity.com/paper/2006.07804/) (2020)

## Built on by (most cited first, 2 of 2)

- [Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-stage Span Labeling](https://mlsanity.com/paper/2112.09488/) (2021)
- [Is word segmentation necessary for Vietnamese sentiment classification?](https://mlsanity.com/paper/2301.00418/) (2023)

## Abstract (arXiv)

In this paper, we propose a span labeling approach to model n-gram information for Vietnamese word segmentation, namely SPAN SEG. We compare the span labeling approach with the conditional random field by using encoders with the same architecture. Since Vietnamese and Chinese have similar linguistic phenomena, we evaluated the proposed method on the Vietnamese treebank benchmark dataset and five Chinese benchmark datasets. Through our experimental results, the proposed approach SpanSeg achieves higher performance than the sequence tagging approach with the state-of-the-art F-score of 98.31% on the Vietnamese treebank benchmark, when they both apply the contextual pre-trained language model XLM-RoBERTa and the predicted word boundary information. Besides, we do fine-tuning experiments for the span labeling approach on BERT and ZEN pre-trained language model for Chinese with fewer parameters, faster inference time, and competitive or higher F-scores than the previous state-of-the-art approach, word segmentation with word-hood memory networks, on five Chinese benchmarks.