优化检索增强机器翻译中翻译记忆的示例选择
Optimizing example selection for retrieval-augmented machine translation with translation memories
AI总结：
本研究针对检索增强机器翻译，利用子模函数理论优化翻译记忆示例选择，以最大化源句覆盖率，提升多莱文斯坦变换器的翻译性能。
摘要：
检索增强的机器翻译通过检索相似实例来利用翻译记忆中的示例。这些示例用于调节神经解码器的预测。我们旨在改进上游检索步骤，并考虑一个固定的下游基于编辑的模型：多莱文斯坦变换器。任务在于找到一组最大化源句子整体覆盖率的示例。为此，我们依赖子模函数理论，并探索新的算法来优化这一覆盖率。我们评估了机器翻译任务中由此产生的性能提升。
英文摘要：
Retrieval-augmented machine translation leverages examples from a translation memory by retrieving similar instances. These examples are used to condition the predictions of a neural decoder. We aim to improve the upstream retrieval step and consider a fixed downstream edit-based model: the multi-Levenshtein Transformer. The task consists of finding a set of examples that maximizes the overall coverage of the source sentence. To this end, we rely on the theory of submodular functions and explore new algorithms to optimize this coverage. We evaluate the resulting performance gains for the machine translation task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
