Cross-Encoder Data Annotation for Bi-Encoder Based Product Matching (2022.emnlp-industry)
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| Challenge: | Existing approaches to match seller listed items to appropriate product are computationally heavy and require computational resources. |
| Approach: | They propose a technique to annotate or refine human annotated training data for bi-encoder models using a cross-encoding model. |
| Outcome: | The proposed approach improves 4% absolute accuracy when no training data is available and 2% when annotated training data exists. |
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