Language-agnostic BERT Sentence Embedding
Table 1: [P]recision, [R]ecall and [F]-score of BUCC training set score with cosine similarity scores. The thresholds are chosen for the best F scores on the training set. Following the naming of BUCC task Zweigenbaum et al. (2018), we treat en as the target and the other language as source in forward search. Backward is vice versa.
['[EMPTY]', '[BOLD] Models', '[BOLD] fr-en P', '[BOLD] fr-en R', '[BOLD] fr-en F', '[BOLD] de-en P', '[BOLD] de-en R', '[BOLD] de-en F', '[BOLD] ru-en P', '[BOLD] ru-en R', '[BOLD] ru-en F', '[BOLD] zh-en P', '[BOLD] zh-en R', '[BOLD] zh-en F']
and Yang et al. Following the original previous work, we perform both of the forward search and backward search. Where forward search treat en as the target and the other language as source in forward search, and backward is vice versa. The LaBSE outperforms the previous models in all languages. It is worth to note that the previous state-of-the-art Yang et al.