Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM
This work addresses segmentation for Arabic dialects, which is incremental as it applies existing methods to new dialect data.
The paper tackled Arabic word segmentation for four major dialects using limited training data, achieving solid results by comparing SVM ranking and bi-LSTM-CRF sequence labeling methods.
Arabic word segmentation is essential for a variety of NLP applications such as machine translation and information retrieval. Segmentation entails breaking words into their constituent stems, affixes and clitics. In this paper, we compare two approaches for segmenting four major Arabic dialects using only several thousand training examples for each dialect. The two approaches involve posing the problem as a ranking problem, where an SVM ranker picks the best segmentation, and as a sequence labeling problem, where a bi-LSTM RNN coupled with CRF determines where best to segment words. We are able to achieve solid segmentation results for all dialects using rather limited training data. We also show that employing Modern Standard Arabic data for domain adaptation and assuming context independence improve overall results.