Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax
Table 4: P@1 (%) of target document retrieval on the UN corpus. Models attempt to select the true target from the entire corpus (85k documents). We simply take the average of the sentence embeddings from BiDE+AM models as the document embedding.
['Model', 'en-fr', 'en-es', 'en-ru', 'en-zh']
The results of two baseline models are shown in rows 1 and 2. Rows 3 and 4 provide the results for both forward and backward retrieval using document embeddings obtained by averaging BiDE+AM sentence embeddings. Both en-fr and en-es models establish new state-of-the-art performance at 97% P@1. We also list the results of the averaging models for en-ru and en-zh. It is worth noticing that the performance is consistent across all language pairs.