Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging

TL;DR

This paper investigates language-independent segmentation methods for Arabic NLP, demonstrating that data-driven sub-word units, characters, and word embeddings can achieve competitive results in machine translation and POS tagging, reducing reliance on complex, domain-dependent tools.

Contribution

The study introduces and evaluates three novel language-independent segmentation approaches for Arabic, showing they can match or outperform traditional morphological segmentation methods.

Findings

Neural machine translation is sensitive to source-target token ratio.

Language-independent methods achieve near state-of-the-art performance.

A source-target token ratio close to 1 yields optimal translation results.

Abstract

Word segmentation plays a pivotal role in improving any Arabic NLP application. Therefore, a lot of research has been spent in improving its accuracy. Off-the-shelf tools, however, are: i) complicated to use and ii) domain/dialect dependent. We explore three language-independent alternatives to morphological segmentation using: i) data-driven sub-word units, ii) characters as a unit of learning, and iii) word embeddings learned using a character CNN (Convolution Neural Network). On the tasks of Machine Translation and POS tagging, we found these methods to achieve close to, and occasionally surpass state-of-the-art performance. In our analysis, we show that a neural machine translation system is sensitive to the ratio of source and target tokens, and a ratio close to 1 or greater, gives optimal performance.