Language-agnostic BERT Sentence Embedding
Table 5: The top and bottom performance of Tatoeba for languages without training data.
['[BOLD] Language', '[BOLD] Tatoeba Accuracy', '[BOLD] Unknown Token %', '[BOLD] Avg. Token. Length', '[BOLD] Avg. Sent. Length']
There are total of 30+ such languages The performance is surprisingly good for most of the languages with an average accuracy around 60% on those languages. Nearly one third of them have accuracy larger than 75%, and only 7 of them have accuracy lower than 25%. Such positive language transfer across languages is only possible due to the massively multilingual nature of LaBSE. We analyze effect of vocabulary by inspecting the unknown token rate, average token length in characters, and average sentence length in tokens of all languages Arivazhagan et al. The unknown token rates are surprisingly low for all languages, indicating a good coverage of the built vocab. Languages with low performance tend to on average have shorter token lengths and longer sequence lengths indicating which indicates sub-par vocab coverage . A better vocab could potentially benefit those languages.