Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning

• URL: http://arxiv.org/abs/2301.09626v1
• Date: Mon, 23 Jan 2023 18:56:12 GMT
• Title: Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning
• Authors: Malte Ostendorff, Georg Rehm
• Abstract summary: Most Transformer language models are pretrained on English text. As model sizes grow, the performance gap between English and other languages increases even further. We introduce a cross-lingual and progressive transfer learning approach, called CLP-Transfer.
• Score: 0.7612676127275795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most Transformer language models are primarily pretrained on English text, limiting their use for other languages. As the model sizes grow, the performance gap between English and other languages with fewer compute and data resources increases even further. Consequently, more resource-efficient training methods are needed to bridge the gap for languages with fewer resources available. To address this problem, we introduce a cross-lingual and progressive transfer learning approach, called CLP-Transfer, that transfers models from a source language, for which pretrained models are publicly available, like English, to a new target language. As opposed to prior work, which focused on the cross-lingual transfer between two languages, we extend the transfer to the model size. Given a pretrained model in a source language, we aim for a same-sized model in a target language. Instead of training a model from scratch, we exploit a smaller model that is in the target language but requires much fewer resources. Both small and source models are then used to initialize the token embeddings of the larger model based on the overlapping vocabulary of the source and target language. All remaining weights are reused from the model in the source language. This approach outperforms the sole cross-lingual transfer and can save up to 80% of the training steps compared to the random initialization.

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