When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models

• URL: http://arxiv.org/abs/2010.12858v2
• Date: Sat, 17 Apr 2021 09:56:40 GMT
• Title: When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
• Authors: Benjamin Muller and Antonis Anastasopoulos and Beno\^it Sagot and Djam\'e Seddah
• Abstract summary: Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. We show that such models behave in multiple ways on unseen languages.
• Score: 2.457872341625575
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. Transliterating those languages improves very significantly the ability of large-scale multilingual language models on downstream tasks.

Related papers

• LlamaTurk: Adapting Open-Source Generative Large Language Models for Low-Resource Language [2.9914612342004503]
  This study explores an alternative solution by adapting large language models, primarily trained on English, to low-resource languages. We assess various strategies, including continual training, instruction fine-tuning, task-specific fine-tuning, and vocabulary extension. The results show that continual training improves language comprehension, as reflected in perplexity scores, and task-specific tuning generally enhances performance of downstream tasks.
  arXiv  Detail & Related papers  (2024-05-13T13:41:59Z)
• When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages [25.52470575274251]
  We pre-train over 10,000 monolingual and multilingual language models for over 250 languages. We find that in moderation, adding multilingual data improves low-resource language modeling performance. As dataset sizes increase, adding multilingual data begins to hurt performance for both low-resource and high-resource languages.
  arXiv  Detail & Related papers  (2023-11-15T18:47:42Z)
• The Less the Merrier? Investigating Language Representation in Multilingual Models [8.632506864465501]
  We investigate the linguistic representation of different languages in multilingual models. We observe from our experiments that community-centered models perform better at distinguishing between languages in the same family for low-resource languages.
  arXiv  Detail & Related papers  (2023-10-20T02:26:34Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes [15.870989191524094]
  We develop a general approach that requires only unlabelled text to detect which languages are not well understood by a cross-lingual model. Our approach is derived from the hypothesis that if a model's understanding is insensitive to perturbations to text in a language, it is likely to have a limited understanding of that language.
  arXiv  Detail & Related papers  (2022-11-09T16:45:16Z)
• Language Contamination Explains the Cross-lingual Capabilities of English Pretrained Models [79.38278330678965]
  We find that common English pretraining corpora contain significant amounts of non-English text. This leads to hundreds of millions of foreign language tokens in large-scale datasets. We then demonstrate that even these small percentages of non-English data facilitate cross-lingual transfer for models trained on them.
  arXiv  Detail & Related papers  (2022-04-17T23:56:54Z)
• Continual Learning in Multilingual NMT via Language-Specific Embeddings [92.91823064720232]
  It consists in replacing the shared vocabulary with a small language-specific vocabulary and fine-tuning the new embeddings on the new language's parallel data. Because the parameters of the original model are not modified, its performance on the initial languages does not degrade.
  arXiv  Detail & Related papers  (2021-10-20T10:38:57Z)
• Discovering Representation Sprachbund For Multilingual Pre-Training [139.05668687865688]
  We generate language representation from multilingual pre-trained models and conduct linguistic analysis. We cluster all the target languages into multiple groups and name each group as a representation sprachbund. Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.
  arXiv  Detail & Related papers  (2021-09-01T09:32:06Z)
• 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)
• Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank [46.626315158735615]
  Pretrained multilingual contextual representations have shown great success, but due to the limits of their pretraining data, their benefits do not apply equally to all language varieties. This presents a challenge for language varieties unfamiliar to these models, whose labeled emphand unlabeled data is too limited to train a monolingual model effectively. We propose the use of additional language-specific pretraining and vocabulary augmentation to adapt multilingual models to low-resource settings.
  arXiv  Detail & Related papers  (2020-09-29T16:12:52Z)
• Cross-lingual, Character-Level Neural Morphological Tagging [57.0020906265213]
  We train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together. Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones, improving accuracy by up to 30% over a monolingual model.
  arXiv  Detail & Related papers  (2017-08-30T08:14:34Z)

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.