Improving Cross-Lingual Transfer in Multilingual Language Models by Aligning Different Scripts with Transliteration

Khái niệm cốt lõi

Transliteration-based post-training alignment improves cross-lingual transfer in multilingual language models, especially between related languages with different scripts, by aligning representations in the original and Latin scripts.

Tóm tắt

• Bibliographic Information: Xhelili, O., Liu, Y., & Schütze, H. (2024). Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment. arXiv preprint arXiv:2406.19759v2.
• Research Objective: This paper investigates the use of transliteration as a post-training alignment method to improve cross-lingual transfer in multilingual pre-trained language models (mPLMs), particularly for related languages written in different scripts.
• Methodology: The authors propose a novel transliteration-based post-training alignment (PPA) method that operates on both sentence and token levels. They fine-tune the Glot500 mPLM using this method on two language groups: Mediterranean-Amharic-Farsi and South+East Asian Languages. The effectiveness of the aligned models is evaluated on various downstream tasks, including sentence retrieval, text classification, and sequence labeling, using a zero-shot cross-lingual transfer setup with English and three other source languages from each group.
• Key Findings: The PPA method consistently improves the performance of Glot500 across different downstream tasks and language groups. The improvements are particularly significant for the sentence retrieval task. The study also highlights the importance of selecting appropriate source languages for cross-lingual transfer, with in-group high-resource languages often outperforming English.
• Main Conclusions: Transliteration-based post-training alignment is an effective technique for enhancing cross-lingual transfer in mPLMs, especially for related languages facing script barriers. The proposed PPA method, incorporating both sentence and token-level alignments, demonstrates consistent improvements across various tasks and language groups.
• Significance: This research contributes to the field of cross-lingual transfer learning by addressing the script barrier issue in mPLMs. The findings have practical implications for improving the performance of NLP applications in low-resource languages.
• Limitations and Future Research: The study acknowledges limitations related to the transliteration process, which can lead to information loss. Future research could focus on improving transliteration techniques and exploring alternative token-level alignment objectives. Additionally, expanding the vocabulary of mPLMs to include subwords from Latin transliterations could further enhance the effectiveness of the proposed method.

Thống kê

The authors sampled 10% of the available data for each language or a minimum of 10k sentences, whichever was larger, from the Glot500-c training dataset.
The Mediterranean-Amharic-Farsi group consists of 10 languages, 5 scripts, and around 16M sentences.
The South+East Asian Languages group consists of 10 languages, 7 scripts, and around 4M sentences.
The aligned model achieved, on average, more than 20% higher accuracy than the baseline Glot500 model on the SR-B sentence retrieval task.

Trích dẫn

Thông tin chi tiết chính được chắt lọc từ

Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment

by Orge... lúc arxiv.org 10-10-2024

https://arxiv.org/pdf/2406.19759.pdf

Yêu cầu sâu hơn

How might the effectiveness of transliteration-based alignment vary across different language families with varying degrees of linguistic similarity?

The effectiveness of transliteration-based alignment in multilingual pre-trained language models (mPLMs) is highly dependent on the degree of linguistic similarity between the languages involved, particularly within different language families.

Closely Related Languages: For languages within the same family or with significant historical interaction (like those in the paper's Mediterranean-Amharic-Farsi group), transliteration can be highly effective. These languages often share a large portion of their lexicon, and transliteration helps bridge the script barrier, allowing the model to recognize cognates and borrowings more easily. This shared lexical space leads to better cross-lingual transfer for tasks like sentence retrieval, and even token-level tasks like NER, as the phonetic nature of the scripts aligns well with Latin.

Distantly Related Languages: The benefits diminish when aligning languages from different families with limited shared vocabulary. Transliteration might not be as beneficial here, as the underlying linguistic structures and semantic spaces differ significantly. The model might struggle to find meaningful alignments, even with a common script. For example, aligning Vietnamese (Austroasiatic) with Arabic (Semitic) through transliteration might not yield significant improvements due to minimal lexical overlap and vastly different grammar.

Morphological Complexity: Languages with complex morphology pose a challenge. Transliteration often operates at a surface level, potentially obscuring rich morphological information crucial for tasks like POS tagging or dependency parsing. Aligning Finnish (highly agglutinative) with English (relatively isolating) might be less effective, as the transliteration wouldn't capture the nuances of Finnish word formation.
In essence, transliteration-based alignment is most effective when the languages involved have a high degree of lexical overlap and relatively similar phonetic structures. As linguistic distance increases, the benefits decrease, and alternative alignment strategies might be more appropriate.

Could the use of transliteration introduce biases or inaccuracies, particularly for languages with complex phonetic and morphological systems?

Yes, the use of transliteration in NLP, while beneficial for cross-lingual transfer, can introduce biases and inaccuracies, especially for languages with complex phonetic and morphological systems.

Phonetic Ambiguity: Transliteration often simplifies the phonetic representation of a language, potentially mapping multiple sounds to a single character in the target script. This can lead to ambiguity and impact downstream tasks like speech recognition or pronunciation modeling. For example, the Arabic letter 'ح' can be transliterated as 'h' or 'ḥ,' depending on pronunciation, potentially causing confusion.

Morphological Oversimplification: As mentioned earlier, transliteration might not adequately represent the morphological complexities of a language. Agglutinative languages, where morphemes (meaningful units) are strung together, can lose crucial information during transliteration. This can lead to inaccurate POS tagging, parsing errors, and misinterpretations of meaning.

Bias Towards High-Resource Languages: Most transliteration systems are primarily developed and tested on high-resource languages, potentially leading to biases. A system trained on a large corpus of English-French transliterations might not perform as accurately for low-resource languages with unique phonetic and orthographic rules.

Loss of Cultural Nuance: Scripts are more than just writing systems; they carry cultural and historical significance. Transliteration can inadvertently lead to a loss of this nuance, homogenizing languages and potentially contributing to the erasure of linguistic diversity.
To mitigate these issues, it's crucial to:

Develop Phonetically Aware Systems: Transliteration models should consider phonetic variations and strive for greater accuracy in representing sounds.
Incorporate Morphological Information: Integrating morphological analysis into transliteration can help preserve crucial linguistic information.
Focus on Low-Resource Languages: More research and resources should be directed towards developing robust transliteration systems for under-resourced languages.

What are the potential ethical implications of using transliteration in NLP, especially considering its impact on language diversity and preservation?

The use of transliteration in NLP, while offering technical advantages, raises important ethical considerations, particularly concerning its impact on language diversity and preservation.

Erosion of Linguistic Diversity: Over-reliance on transliteration, especially into dominant scripts like Latin, can contribute to the marginalization of less-used writing systems. If NLP tools and resources primarily focus on transliterated text, it might disincentivize the use and development of resources for languages in their original scripts.

Cultural Appropriation and Misrepresentation: Transliteration, if not done sensitively, can lead to the misrepresentation of a language's sounds and cultural nuances. This is particularly relevant for languages with sacred scripts or those that have faced historical oppression.

Exclusion of Communities: If NLP technologies primarily rely on transliterated text, it can create barriers for communities that are not literate in the target script. This can exacerbate existing digital divides and limit access to information and services.

Impact on Language Revitalization: For endangered languages, the focus on transliteration might divert efforts and resources away from initiatives focused on revitalizing the original script and promoting literacy within the community.
To address these ethical concerns:

Prioritize Original Scripts: NLP research and development should prioritize working with languages in their original scripts whenever possible.
Involve Language Communities: Engage with communities whose languages are being transliterated to ensure their perspectives are considered and their rights are respected.
Promote Script Diversity: Actively support the development of NLP tools and resources for a wide range of scripts, not just dominant ones.
Raise Awareness: Educate the NLP community about the ethical implications of transliteration and promote responsible use of this technology.
By carefully considering these ethical implications and taking proactive steps to mitigate potential harm, the NLP community can harness the benefits of transliteration while respecting linguistic diversity and promoting language preservation.