Adapting XLM-RoBERTa for Ancient and Historical Languages: Insights from the TartuNLP Submission to the SIGTYP 2024 Shared Task

核心概念

The authors present a parameter-efficient fine-tuning approach based on the adapters framework to adapt the XLM-RoBERTa language model for various natural language processing tasks on 16 ancient and historical languages.

要約

The authors describe their submission to the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages. They developed a simple, uniform, and computationally lightweight approach based on the adapters framework using parameter-efficient fine-tuning.

The key highlights are:

1. The authors applied the same adapter-based approach uniformly to all tasks (morphological annotation, POS-tagging, lemmatization, character- and word-level gap-filling) and 16 languages by fine-tuning stacked language- and task-specific adapters on top of the XLM-RoBERTa model.

2. For languages with scripts underrepresented in the XLM-RoBERTa vocabulary, the authors trained custom tokenizers and initialized the embedding layers using lexical overlap with the original multilingual embeddings.

3. The authors' submission obtained an overall second place out of three submissions, with the first place in the word-level gap-filling task.

4. The results demonstrate the feasibility of adapting language models pre-trained on modern languages to historical and ancient languages via the adapter training approach, despite the limited data available for these low-resource languages.

5. The authors note that the performance is lower for languages requiring custom tokenizers and embeddings, suggesting that more sophisticated approaches to embedding initialization and tokenizer training could further improve the results.

6. The authors also discuss the strengths and limitations of their algorithmic approach to character-level gap-filling, which performed well except for Classical Chinese.

統計

The dataset provided by the organizers comprises 16 ancient and historical languages spanning several historical epochs and upper bounded by 1700 CE.

引用

"The application of natural language processing techniques and pre-trained language models to analysis of ancient and historical languages is a compelling subject of research that has been so far overlooked."
"Large pre-trained language models, however, are predominantly trained on corpora of modern languages, with few exceptions such as Latin-BERT."
"Our submission obtained an overall second place out of three submissions, with the first place in word-level gap-filling."

抽出されたキーインサイト

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

by Aleksei Dork... 場所 arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12845.pdf

深掘り質問

How could the adapter-based approach be further extended to leverage typological relatedness between ancient/historical languages and their modern counterparts

To further extend the adapter-based approach to leverage typological relatedness between ancient/historical languages and their modern counterparts, several strategies can be implemented. Firstly, incorporating language-specific adapters that capture typological features unique to each language can enhance the model's ability to adapt to linguistic differences. By fine-tuning these adapters on typologically related languages, the model can learn to generalize across language families or language groups. Additionally, creating a shared adapter space where adapters from related languages can interact and share information can facilitate cross-lingual transfer of linguistic features. This shared space can serve as a bridge for transferring knowledge between languages with similar typological characteristics. Moreover, incorporating transfer learning techniques that focus on typological similarities, such as multi-task learning on typologically related tasks, can further enhance the model's ability to adapt to diverse linguistic structures.

What are the potential limitations of the adapter-based approach in handling languages with significantly different writing systems or morphological complexity compared to the modern languages used in pre-training

The adapter-based approach may face potential limitations when handling languages with significantly different writing systems or morphological complexity compared to the modern languages used in pre-training. One limitation is the reliance on subword tokenization, which may not adequately capture the unique morphological features of languages with complex morphological structures. Subword tokenization may struggle to represent intricate morphological phenomena present in languages with rich inflectional or agglutinative systems. Additionally, languages with non-alphabetic scripts or logographic writing systems may pose challenges for the model in terms of tokenization and representation of characters. The model may struggle to capture the semantic and syntactic nuances encoded in the characters of such languages, leading to suboptimal performance in tasks requiring understanding of complex writing systems.

Could the authors' insights on adapting language models to low-resource ancient and historical languages be applied to other domains with limited data, such as specialized technical or scientific corpora

The insights provided by the authors on adapting language models to low-resource ancient and historical languages can be applied to other domains with limited data, such as specialized technical or scientific corpora. By leveraging the adapter-based approach, researchers can fine-tune pre-trained language models on domain-specific tasks and datasets, enabling the models to adapt to the specialized vocabulary and linguistic patterns of technical or scientific texts. Additionally, customizing tokenizers and embeddings for domain-specific languages or terminology can enhance the model's ability to process and understand domain-specific content. By training task-specific adapters and language adapters on specialized corpora, language models can be tailored to effectively handle low-resource domains and achieve improved performance on domain-specific tasks.