Abstract
Standard context-aware neural machine translation (NMT) typically relies on parallel document-level data, exploiting both source and target contexts. Concatenation-based approaches in particular, still a strong baseline for document-level NMT, prepend source and/or target context sentences to the sentences to be translated, with model variants that exploit equal amounts of source and target data on each side achieving state-of-the-art results. In this work, we investigate whether target data should be further promoted within standard concatenation-based approaches, as most document-level phenomena rely on information that is present on the target language side. We evaluate novel concatenation-based variants where the target context is prepended to the source language, either in isolation or in combination with the source context. Experimental results in English-Russian and Basque-Spanish show that including target context in the source leads to large improvements on target language phenomena. On source-dependent phenomena, using only target language context in the source achieves parity with state-of-the-art concatenation approaches, or slightly underperforms, whereas combining source and target context on the source side leads to significant gains across the board.
Abstract (translated)
标准上下文感知神经机器翻译（NMT）通常依赖于并行文档级别数据，并利用源语和目标语上下文。特别是，基于连接的方法在文档级别NMT中仍然是一个强大的基线，将源语和/或目标语上下文句子附加到要翻译的句子前面，利用等量的源语和目标语数据实现达到最先进水平的模型变体。在这项工作中，我们研究了是否应该在标准连接方法中进一步促进目标数据，因为大多数文档级别现象都依赖于目标语言侧存在的信息。我们评估了新颖的连接基线变体，其中目标上下文都被附加到源语言。在英语-俄语和Basque-西班牙语实验中，将目标上下文包含在源语言中，在目标语言现象方面取得了很大的改进。在源相关现象上，仅使用目标语上下文在源中实现与最先进连接方法达到平衡，或者稍有落后，而将源和目标语上下文在源侧结合起来，产生了显著的全面改进。
URL
https://arxiv.org/abs/2402.06342
