TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages

• URL: http://arxiv.org/abs/2404.12845v2
• Date: Mon, 09 Dec 2024 13:30:56 GMT
• Title: TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages
• Abstract summary: We present our submission to the unconstrained subtask of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages.<n>We developed a simple, uniform, and computationally lightweight approach based on the adapters framework using parameter-efficient fine-tuning.<n>Our results show the feasibility of adapting language models pre-trained on modern languages to historical and ancient languages via adapter training.
• Score: 0.21485350418225246
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present our submission to the unconstrained subtask of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages for morphological annotation, POS-tagging, lemmatization, character- and word-level gap-filling. We developed a simple, uniform, and computationally lightweight approach based on the adapters framework using parameter-efficient fine-tuning. We applied the same adapter-based approach uniformly to all tasks and 16 languages by fine-tuning stacked language- and task-specific adapters. Our submission obtained an overall second place out of three submissions, with the first place in word-level gap-filling. Our results show the feasibility of adapting language models pre-trained on modern languages to historical and ancient languages via adapter training.

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