TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models

• URL: http://arxiv.org/abs/2401.06620v2
• Date: Thu, 23 May 2024 13:30:09 GMT
• Title: TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language Models
• Authors: Yihong Liu, Chunlan Ma, Haotian Ye, Hinrich Schütze,
• Abstract summary: We propose TransliCo to fine-tune an mPLM by contrasting sentences in its training data and their transliterations in a unified script. We show that Furina outperforms the original Glot500-m on various zero-shot crosslingual transfer tasks.
• Score: 50.40191599304911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The world's more than 7000 languages are written in at least 293 scripts. Due to various reasons, many closely related languages use different scripts, which poses a difficulty for multilingual pretrained language models (mPLMs) in learning crosslingual knowledge through lexical overlap. As a consequence, mPLMs are faced with a script barrier: representations from different scripts are located in different subspaces, which can result in crosslingual transfer involving languages of different scripts performing suboptimally. To address this problem, we propose TransliCo, a framework that optimizes the Transliteration Contrastive Modeling (TCM) objective to fine-tune an mPLM by contrasting sentences in its training data and their transliterations in a unified script (in our case Latin), which enhances uniformity in the representation space for different scripts. Using Glot500-m, an mPLM pretrained on over 500 languages, as our source model, we fine-tune it on a small portion (5%) of its training data, and refer to the resulting model as Furina. We show that Furina not only better aligns representations from distinct scripts but also outperforms the original Glot500-m on various zero-shot crosslingual transfer tasks. Additionally, we achieve consistent improvement in a case study on the Indic group where the languages exhibit areal features but use different scripts. We make our code and models publicly available.

Related papers

• Exploring the Role of Transliteration in In-Context Learning for Low-resource Languages Written in Non-Latin Scripts [50.40191599304911]
  We investigate whether transliteration is also effective in improving LLMs' performance for low-resource languages written in non-Latin scripts. We propose three prompt templates, where the target-language text is represented in (1) its original script, (2) Latin script, or (3) both. Our findings show that the effectiveness of transliteration varies by task type and model size.
  arXiv  Detail & Related papers  (2024-07-02T14:51:20Z)
• Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment [50.27950279695363]
  The transfer performance is often hindered when a low-resource target language is written in a different script than the high-resource source language. Inspired by recent work that uses transliteration to address this problem, our paper proposes a transliteration-based post-pretraining alignment (PPA) method.
  arXiv  Detail & Related papers  (2024-06-28T08:59:24Z)
• XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations [25.50509874992198]
  Cross-Lingual Semantic Parsing aims to translate queries in multiple natural languages into meaning representations. Existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications. We present XSemPLR, a unified benchmark for cross-lingual semantic parsing featured with 22 natural languages and 8 meaning representations.
  arXiv  Detail & Related papers  (2023-06-07T01:09:37Z)
• Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning [98.60739735409243]
  Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks. We introduce XSGD for cross-lingual alignment pretraining, a parallel and large-scale multilingual conversation dataset. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts.
  arXiv  Detail & Related papers  (2023-04-03T18:46:01Z)
• Curriculum Script Distillation for Multilingual Visual Question Answering [10.721189858694396]
  We introduce a curriculum based on the source and target language translations to finetune the pre-trained models for the downstream task. We show that target languages that share the same script perform better (6%) than other languages and mixed-script code-switched languages perform better than their counterparts.
  arXiv  Detail & Related papers  (2023-01-17T23:55:50Z)
• Does Transliteration Help Multilingual Language Modeling? [0.0]
  We empirically measure the effect of transliteration on Multilingual Language Models. We focus on the Indic languages, which have the highest script diversity in the world. We find that transliteration benefits the low-resource languages without negatively affecting the comparatively high-resource languages.
  arXiv  Detail & Related papers  (2022-01-29T05:48:42Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)
• FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding [85.29270319872597]
  We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
  arXiv  Detail & Related papers  (2020-09-10T22:42:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.