Abstract
Matching a seller listed item to an appropriate product is an important step for an e-commerce platform. With the recent advancement in deep learning, there are different encoder based approaches being proposed as solution. When textual data for two products are available, cross-encoder approaches encode them jointly while bi-encoder approaches encode them separately. Since cross-encoders are computationally heavy, approaches based on bi-encoders are a common practice for this challenge. In this paper, we propose cross-encoder data annotation; a technique to annotate or refine human annotated training data for bi-encoder models using a cross-encoder model. This technique enables us to build a robust model without annotation on newly collected training data or further improve model performance on annotated training data. We evaluate the cross-encoder data annotation on the product matching task using a real-world e-commerce dataset containing 104 million products. Experimental results show that the cross-encoder data annotation improves 4% absolute accuracy when no annotation for training data is available, and 2% absolute accuracy when annotation for training data is available.- Anthology ID:
- 2022.emnlp-industry.16
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, UAE
- Editors:
- Yunyao Li, Angeliki Lazaridou
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 161–168
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-industry.16
- DOI:
- 10.18653/v1/2022.emnlp-industry.16
- Cite (ACL):
- Justin Chiu and Keiji Shinzato. 2022. Cross-Encoder Data Annotation for Bi-Encoder Based Product Matching. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 161–168, Abu Dhabi, UAE. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Encoder Data Annotation for Bi-Encoder Based Product Matching (Chiu & Shinzato, EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.emnlp-industry.16.pdf