Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
Table 4: Ablation study of cross-lingual alignment, where mono refers to without cross-lingual alignment learning, en2ch refers to calculating the matching score in the Chinese bilingual space, and ch2en is to compute the relevance score in the English Bilingual Space.
['[BOLD] Model', '[ITALIC] R2@1', '[ITALIC] R10@1', '[ITALIC] R10@2', '[ITALIC] R10@5', '[ITALIC] MRR']
Through analyzing, we observe that monolingual BERT is superior to train domain-specific word embeddings in our task. We attribute such an observation to the relatively small size of the domain-specific monolingual data, which is far from ideal to train a decent model. As for the influence of cross-lingual alignment, it can effectively capture the correlations between two different languages and thus can introduce performance improvement. Besides, we also notice that the context-dependent mapping strategy is more effective in English bilingual space. This observation is perhaps owing to that the relatively small size of the unordered Chinese product attribute can be easily mapped to another language space compared with a relatively long product description with lexical and syntactic structures.