Language-agnostic BERT Sentence Embedding

We adapt multilingual BERT to produce language-agnostic sentence embeddings for 109 languages. %The state-of-the-art for numerous monolingual and multilingual NLP tasks is masked language model (MLM) pretraining followed by task specific fine-tuning. While English sentence embeddings have been obtained by fine-tuning a pretrained BERT model, such models have not been applied to multilingual sentence embeddings. Our model combines masked language model (MLM) and translation language model (TLM) pretraining with a translation ranking task using bi-directional dual encoders. The resulting multilingual sentence embeddings improve average bi-text retrieval accuracy over 112 languages to 83.7%, well above the 65.5% achieved by the prior state-of-the-art on Tatoeba. Our sentence embeddings also establish new state-of-the-art results on BUCC and UN bi-text retrieval.