# Effective Subword Segmentation for Text Comprehension

arXiv: https://arxiv.org/abs/1811.02364 · HTML: https://mlsanity.com/paper/1811.02364/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge](https://mlsanity.com/paper/2102.05474/) (2021)
- [N-Gram Unsupervised Compoundation and Feature Injection for Better Symbolic Music Understanding](https://mlsanity.com/paper/2312.08931/) (2023)
- [Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models](https://mlsanity.com/paper/2505.19631/) (2025)

## Abstract (arXiv)

Representation learning is the foundation of machine reading comprehension and inference. In state-of-the-art models, character-level representations have been broadly adopted to alleviate the problem of effectively representing rare or complex words. However, character itself is not a natural minimal linguistic unit for representation or word embedding composing due to ignoring the linguistic coherence of consecutive characters inside word. This paper presents a general subword-augmented embedding framework for learning and composing computationally-derived subword-level representations. We survey a series of unsupervised segmentation methods for subword acquisition and different subword-augmented strategies for text understanding, showing that subword-augmented embedding significantly improves our baselines in various types of text understanding tasks on both English and Chinese benchmarks.