{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/self-training-pre-trained-language-models-for","title":"Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling","arxiv_id":"2101.04758","date":"2021-01-12","proceeding":"EACL 2021 2","authors":["Muhammad Khalifa","Muhammad Abdul-Mageed","Khaled Shaalan"],"abstract":"A sufficient amount of annotated data is usually required to fine-tune pre-trained language models for downstream tasks. Unfortunately, attaining labeled data can be costly, especially for multiple language varieties and dialects. We propose to self-train pre-trained language models in zero- and few-shot scenarios to improve performance on data-scarce varieties using only resources from data-rich ones. We demonstrate the utility of our approach in the context of Arabic sequence labeling by using a language model fine-tuned on Modern Standard Arabic (MSA) only to predict named entities (NE) and part-of-speech (POS) tags on several dialectal Arabic (DA) varieties. We show that self-training is indeed powerful, improving zero-shot MSA-to-DA transfer by as large as \\texttildelow 10\\% F$_1$ (NER) and 2\\% accuracy (POS tagging). We acquire even better performance in few-shot scenarios with limited amounts of labeled data. We conduct an ablation study and show that the performance boost observed directly results from the unlabeled DA examples used for self-training. Our work opens up opportunities for developing DA models exploiting only MSA resources and it can be extended to other languages and tasks. Our code and fine-tuned models can be accessed at https://github.com/mohammadKhalifa/zero-shot-arabic-dialects.","url_abs":"https://arxiv.org/abs/2101.04758v4","url_pdf":"https://arxiv.org/pdf/2101.04758v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"self-training-pre-trained-language-models-for","repo_url":"https://github.com/mohammadKhalifa/zero-shot-arabic-dialects","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"pos","task_name":"POS"},{"task_slug":"pos-tagging","task_name":"POS Tagging"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2101.04758","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}