用深度学习方法预测翻译记忆中的锚定词，提升机器翻译效率。
Predicting Anchored Text from Translation Memories for Machine Translation Using Deep Learning Methods
- 基于Word2Vec、BERT和GPT-4等模型，从翻译记忆中预测锚定词。
- 在法语转英语任务中，部分情况下效果优于神经机器翻译。
- 适合需要高效处理重复翻译片段的CAT工具开发者或译员。
翻译记忆（TMs）是计算机辅助翻译（CAT）工具的核心。使用CAT工具时，译者通过模糊匹配算法在TM中查找与待译段落（s'）相近的原文段落（s），并获取对应的源语言与目标语言平行语料（s, t）。许多CAT工具还采用模糊匹配修复（FMR）技术，利用这些平行语料自动生成新条目，以适应修改后的原文。传统FMR依赖机器翻译来修正需改动的词汇。本文表明，对于遵循连续词袋（CBOW）模式的锚定词，可采用基于深度学习的方法如Word2Vec、BERT和ChatGPT进行更优或相当的翻译。实验显示，在法语到英语的翻译任务中，这些方法在部分情况下表现优于神经机器翻译。
原文摘要 · Abstract (English)
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.
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