Zero-Resource Multi-Dialectal Arabic Natural Language Understanding

• URL: http://arxiv.org/abs/2104.06591v1
• Date: Wed, 14 Apr 2021 02:29:27 GMT
• Title: Zero-Resource Multi-Dialectal Arabic Natural Language Understanding
• Authors: Muhammad Khalifa and Hesham Hassan and Aly Fahmy
• Abstract summary: We investigate the zero-shot performance on Dialectal Arabic (DA) when fine-tuning a pre-trained language model on modern standard Arabic (MSA) data only. We propose self-training with unlabeled DA data and apply it in the context of named entity recognition (NER), part-of-speech (POS) tagging, and sarcasm detection (SRD) Our results demonstrate the effectiveness of self-training with unlabeled DA data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A reasonable amount of annotated data is required for fine-tuning pre-trained language models (PLM) on downstream tasks. However, obtaining labeled examples for different language varieties can be costly. In this paper, we investigate the zero-shot performance on Dialectal Arabic (DA) when fine-tuning a PLM on modern standard Arabic (MSA) data only -- identifying a significant performance drop when evaluating such models on DA. To remedy such performance drop, we propose self-training with unlabeled DA data and apply it in the context of named entity recognition (NER), part-of-speech (POS) tagging, and sarcasm detection (SRD) on several DA varieties. Our results demonstrate the effectiveness of self-training with unlabeled DA data: improving zero-shot MSA-to-DA transfer by as large as \texttildelow 10\% F$_1$ (NER), 2\% accuracy (POS tagging), and 4.5\% F$_1$ (SRD). We conduct an ablation experiment and show that the performance boost observed directly results from the unlabeled DA examples used for self-training. Our work opens up opportunities for leveraging the relatively abundant labeled MSA datasets to develop DA models for zero and low-resource dialects. We also report new state-of-the-art performance on all three tasks and open-source our fine-tuned models for the research community.

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