Language-agnostic BERT Sentence Embedding
Table 2: P@1 on UN parallel sentence retrieval task.
['[BOLD] Model', '[BOLD] Langs', '[BOLD] en-es', '[BOLD] en-fr', '[BOLD] en-ru', '[BOLD] en-zh']
For each non-English language, we iterate over English sentences to find the translation sentence from the entire sentence pool from the other language. We compare the proposed model with the current state-of-the-art bilingual models from Yang et al. (m-USE) model with the transformer architecture. Again LaBSE shows the new state-of-the-art performance on 3 of the 4 languages, with P@1 91.1, 88.3, 90.8 for en-es, en-fr, en-ru respectively. The zh-en of LaBSE reaches 87.7, only 0.2 lower than the best bilingual model and nearly 9 points better than the previous best multilingual model.