Abstract

While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning Reimers and Gurevych 2019, BERT based cross-lingual sentence embeddings have yet to be explored. We systematically investigate methods for learning multilingual sentence embeddings by combining the best methods for learning monolingual and cross-lingual representations including: masked language modeling (MLM), translation language modeling (TLM) Conneau and Lample 2019, dual encoder translation ranking Guo et al. 2018, and additive margin softmax Yang et al. 2019a. We show that introducing a pre-trained multilingual language model dramatically reduces the amount of parallel training data required to achieve good performance by 80%. Composing the best of these methods produces a model that achieves 83.7% bi-text retrieval accuracy over 112 languages on Tatoeba, well above the 65.5% achieved by Artetxe and Schwenk 2019b, while still performing competitively on monolingual transfer learning benchmarks Conneau and Kiela 2018. Parallel data mined from CommonCrawl using our best model is shown to train competitive NMT models for en-zh and en-de. We