GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text
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arXiv
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| Format: | Preprint |
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2024
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| author | Ginn, Michael Tjuatja, Lindia He, Taiqi Rice, Enora Neubig, Graham Palmer, Alexis Levin, Lori |
| author_facet | Ginn, Michael Tjuatja, Lindia He, Taiqi Rice, Enora Neubig, Graham Palmer, Alexis Levin, Lori |
| contents | Language documentation projects often involve the creation of annotated text in a format such as interlinear glossed text (IGT), which captures fine-grained morphosyntactic analyses in a morpheme-by-morpheme format. However, there are few existing resources providing large amounts of standardized, easily accessible IGT data, limiting their applicability to linguistic research, and making it difficult to use such data in NLP modeling.
We compile the largest existing corpus of IGT data from a variety of sources, covering over 450k examples across 1.8k languages, to enable research on crosslingual transfer and IGT generation. We normalize much of our data to follow a standard set of labels across languages.
Furthermore, we explore the task of automatically generating IGT in order to aid documentation projects. As many languages lack sufficient monolingual data, we pretrain a large multilingual model on our corpus. We demonstrate the utility of this model by finetuning it on monolingual corpora, outperforming SOTA models by up to 6.6\%. Our pretrained model and dataset are available on Hugging Face. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06399 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text Ginn, Michael Tjuatja, Lindia He, Taiqi Rice, Enora Neubig, Graham Palmer, Alexis Levin, Lori Computation and Language Language documentation projects often involve the creation of annotated text in a format such as interlinear glossed text (IGT), which captures fine-grained morphosyntactic analyses in a morpheme-by-morpheme format. However, there are few existing resources providing large amounts of standardized, easily accessible IGT data, limiting their applicability to linguistic research, and making it difficult to use such data in NLP modeling. We compile the largest existing corpus of IGT data from a variety of sources, covering over 450k examples across 1.8k languages, to enable research on crosslingual transfer and IGT generation. We normalize much of our data to follow a standard set of labels across languages. Furthermore, we explore the task of automatically generating IGT in order to aid documentation projects. As many languages lack sufficient monolingual data, we pretrain a large multilingual model on our corpus. We demonstrate the utility of this model by finetuning it on monolingual corpora, outperforming SOTA models by up to 6.6\%. Our pretrained model and dataset are available on Hugging Face. |
| title | GlossLM: A Massively Multilingual Corpus and Pretrained Model for Interlinear Glossed Text |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.06399 |