Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
Table 3: Evaluation results of baselines and our models. CLMN− refers to CLMN without using pre-trained BERT.
['[BOLD] Model', '[ITALIC] R2@1', '[ITALIC] R10@1', '[ITALIC] R10@2', '[ITALIC] R10@5', '[ITALIC] MRR']
Overall, our proposed CLMN model achieves the best performance on five evaluation metrics. For simplicity, we mainly discuss the performance of R10@1 in the following part. In details, the advanced unsupervised CLIR models only achieve a fair performance without the collected CLIR-EC dataset. Through utilizing paired data, translation based CLIR models significantly outperform the SOTA unsupervised methods and achieves a decent performance on each metric. Moreover, directly modeling the cross-lingual information retrieval task can further introduce a significant performance improvement over the translation based models. Both the recent proposed POSIT-DRMM and ML-BERT models yield a large increase over the strong translation based models. We also observe that our proposed CLMN− matching model are better than the other matching models and even achieves comparable results with the fine-tuned multi-lingual BERT. With the enhancement of monolingual BERT, our proposed CLMN model outperforms the fine-tuned multi-lingual BERT model.