XLM¶
Overview¶
The XLM model was proposed in Cross-lingual Language Model Pretraining by Guillaume Lample*, Alexis Conneau*. It’s a transformer pre-trained using one of the following objectives:
a causal language modeling (CLM) objective (next token prediction),
a masked language modeling (MLM) objective (Bert-like), or
a Translation Language Modeling (TLM) object (extension of Bert’s MLM to multiple language inputs)
The abstract from the paper is the following:
Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT’16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT’16 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.
Tips:
XLM has many different checkpoints, which were trained using different objectives: CLM, MLM or TLM. Make sure to select the correct objective for your task (e.g. MLM checkpoints are not suitable for generation).
XLM has multilingual checkpoints which leverage a specific lang parameter. Check out the multi-lingual page for more information.