A Multitask Learning Approach for Diacritic Restoration

TL;DR

This paper presents a multitask learning approach for diacritic restoration in Arabic, jointly optimizing related NLP tasks to improve accuracy without relying on complex morphological analyzers.

Contribution

It introduces a novel multitask learning framework that enhances diacritic restoration by jointly modeling word segmentation, POS tagging, and syntactic diacritization.

Findings

Joint models outperform baseline methods.

Achieves performance comparable to complex state-of-the-art models.

Effective in low-resource or dialectal data scenarios.

Abstract

In many languages like Arabic, diacritics are used to specify pronunciations as well as meanings. Such diacritics are often omitted in written text, increasing the number of possible pronunciations and meanings for a word. This results in a more ambiguous text making computational processing on such text more difficult. Diacritic restoration is the task of restoring missing diacritics in the written text. Most state-of-the-art diacritic restoration models are built on character level information which helps generalize the model to unseen data, but presumably lose useful information at the word level. Thus, to compensate for this loss, we investigate the use of multi-task learning to jointly optimize diacritic restoration with related NLP problems namely word segmentation, part-of-speech tagging, and syntactic diacritization. We use Arabic as a case study since it has sufficient data resources for tasks that we consider in our joint modeling. Our joint models significantly outperform the baselines and are comparable to the state-of-the-art models that are more complex relying on morphological analyzers and/or a lot more data (e.g. dialectal data).