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This paper presents a novel training method, Conditional Masked LanguageModeling (CMLM), to effectively learn sentence representations on large scaleunlabeled corpora. CMLM integrates sentence representation learning into MLMtraining by conditioning on the encoded vectors of adjacent sentences. OurEnglish CMLM model achieves state-of-the-art performance on SentEval, evenoutperforming models learned using supervised signals. As a fully unsupervisedlearning method, CMLM can be conveniently extended to a broad range oflanguages and domains. We find that a multilingual CMLM model co-trained withbitext retrieval (BR) and natural language inference (NLI) tasks outperformsthe previous state-of-the-art multilingual models by a large margin, e.g. 10%improvement upon baseline models on cross-lingual semantic search. We explorethe same language bias of the learned representations, and propose a simple,post-training and model agnostic approach to remove the language identifyinginformation from the representation while still retaining sentence semantics.

Universal Sentence Representation Learning with Conditional Masked Language Model