Language-agnostic BERT Sentence Embedding
Table 7: Semantic Textual Similarity (STS) benchmarkCer et al. (2017) performance as measured by Pearson’s r.
['[BOLD] Model', '[BOLD] dev', '[BOLD] test']
versus existing sentence embedding models. Following prior work, the semantic similarity of a sentence pair according to LaBSE is computed as the arccos distance between the pair’s sentence embeddings. In addition to performance using similarity scores commuted directly from sentence embeddings from the various models, we include numbers for SentenceBERT when it is fine-tuned for the STS task as well as ConvEmbed when an additional affine transform is trained to fit the embeddings to STS. Rather than directly measuring sentence embeddings quality, tuning toward STS informs the extent to which the information required to assess semantic similarity is captured in anyway within the model.