Abstract
Multilingual search is indispensable for a seamless e-commerce experience. E-commerce search engines typically support multilingual search by cascading a machine translation step before searching the index in its primary language. In practice, search query translation usually involves a translation memory matching step before machine translation. A translation memory (TM) can (i) effectively enforce terminologies for specific brands or products (ii) reduce the computation footprint and latency for synchronous translation and, (iii) fix machine translation issues that cannot be resolved easily or quickly without retraining/tuning the machine translation engine in production. In this abstract, we will propose (1) a method of improving MT query translation using such TM entries when the TM entries are only sub-strings of a customer search query, and (2) an approach to selecting TM entries using search signals that can contribute to better search results.- Anthology ID:
- 2022.amta-upg.9
- Volume:
- Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)
- Month:
- September
- Year:
- 2022
- Address:
- Orlando, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 123–131
- Language:
- URL:
- https://aclanthology.org/2022.amta-upg.9
- DOI:
- Cite (ACL):
- Bryan Zhang. 2022. Improve MT for Search with Selected Translation Memory using Search Signals. In Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track), pages 123–131, Orlando, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Improve MT for Search with Selected Translation Memory using Search Signals (Zhang, AMTA 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.amta-upg.9.pdf