Shivendra Bhardwa


2022

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Refining an Almost Clean Translation Memory Helps Machine Translation
Shivendra Bhardwa | David Alfonso-Hermelo | Philippe Langlais | Gabriel Bernier-Colborne | Cyril Goutte | Michel Simard
Proceedings of the 15th biennial conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

While recent studies have been dedicated to cleaning very noisy parallel corpora to improve Machine Translation training, we focus in this work on filtering a large and mostly clean Translation Memory. This problem of practical interest has not received much consideration from the community, in contrast with, for example, filtering large web-mined parallel corpora. We experiment with an extensive, multi-domain proprietary Translation Memory and compare five approaches involving deep-, feature-, and heuristic-based solutions. We propose two ways of evaluating this task, manual annotation and resulting Machine Translation quality. We report significant gains over a state-of-the-art, off-the-shelf cleaning system, using two MT engines.