Cross-lingual Information Retrieval with BERT
Table 3: Performance of MAP scores on the MATERIAL analysis set and Q1 queries.
['Approach', 'phrase query subset', 'entire query set']
In the table, we report MAP scores on the phrase query subset and the entire query set separately, to see how our model trained with single-word queries performs on query phrases. In the model training stage, QRANN model is the only model that is trained with the query phrases directly, all other models (including BERT) in this experiment will split a multi-word query or query phrase into multiple single-word queries. Surprisingly, the BERT MAP scores for the phrase query subset is the best compared with the performances of other approaches. It shows that BERT model can produce better relevance model for single-word queries and foreign-language sentence. The table also shows that BERT outperforms the other neural approaches over the entire query set.