"Semantic Textual Similarity based on Deep Learning: Can it improve matching and retrieval for Translation Memory tools?"

by Tharindu Ranasinghe, University of Wolverhampton

Update: the event has now finished (Jan 20th 2021).

Abstract

Matching and retrieving previously translated segments from a Translation Memory is the key functionality in Translation Memories systems. However, this matching and retrieving process is still limited to algorithms based on edit distance which we have identified as a major drawback in Translation Memories systems. In this talk, we talk about our research [1,2] on sentence encoders to improve the matching and retrieving process in Translation Memories systems - an effective and efficient solution to replace edit distance-based algorithms.

References:

• Ranasinghe, T. Mitkov, R., Orasan, C. and Caro, R., 2020, May. Semantic Textual Similarity based on Deep Learning: Can it improve matching and retrieval for Translation Memory tools?. In Parallel Corpora: Creation and Applications. John Benjamins.
• Ranasinghe, T. Orasan, C. and Mitkov, R., 2020, May. Intelligent Translation Memory Matching and Retrieval with Sentence Encoders. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (pp. 175–184).