Predicting Anchored Text from Translation Memories for Machine Translation Using Deep Learning Methods

TL;DR

This paper explores using deep learning models like Word2Vec, BERT, and ChatGPT to predict anchored words in translation memories, aiming to improve machine translation quality for CAT tools.

Contribution

It demonstrates that deep learning techniques can effectively predict anchored words, offering an alternative to neural machine translation in translation memory repair.

Findings

Word2Vec, BERT, and ChatGPT achieve comparable or better results than neural machine translation.

Deep learning models effectively predict anchored words in translation memories.

The approach enhances translation memory repair for French-English translation.

Abstract

Translation memories (TMs) are the backbone for professional translation tools called computer-aided translation (CAT) tools. In order to perform a translation using a CAT tool, a translator uses the TM to gather translations similar to the desired segment to translate (s'). Many CAT tools offer a fuzzy-match algorithm to locate segments (s) in the TM that are close in distance to s'. After locating two similar segments, the CAT tool will present parallel segments (s, t) that contain one segment in the source language along with its translation in the target language. Additionally, CAT tools contain fuzzy-match repair (FMR) techniques that will automatically use the parallel segments from the TM to create new TM entries containing a modified version of the original with the idea in mind that it will be the translation of s'. Most FMR techniques use machine translation as a way of "repairing" those words that have to be modified. In this article, we show that for a large part of those words which are anchored, we can use other techniques that are based on machine learning approaches such as Word2Vec. BERT, and even ChatGPT. Specifically, we show that for anchored words that follow the continuous bag-of-words (CBOW) paradigm, Word2Vec, BERT, and GPT-4 can be used to achieve similar and, for some cases, better results than neural machine translation for translating anchored words from French to English.