论文标题
在神经机器翻译培训目标中使用上下文
Using Context in Neural Machine Translation Training Objectives
论文作者
论文摘要
我们使用具有批处理级文档的文档级指标介绍神经机器翻译（NMT）培训。 NMT训练的先前序列对象方法仅关注句子级指标，例如句子bleu，这些指标与所需的评估指标不符，通常是文档BLEU。同时，对文档级NMT培训的研究集中在数据或模型架构上，而不是培训程序。我们发现，这些研究线中的每一个都有一个清晰的空间，并建议将它们与允许文档级评估指标用于NMT培训目标的方案合并。 我们首先从句子样本中示例伪用户。然后，我们将预期的文档BLEU梯度和蒙特卡洛采样用于最低风险培训（MRT）的成本功能。这种两级抽样程序可在序列MRT和最大样子训练上获得NMT性能的提高。我们证明，培训对文档级指标比序列指标更强大。我们进一步证明了使用GLEU使用TER和语法误差校正（GEC）对NMT的改进，这两个指标均在文档级别用于评估。
We present Neural Machine Translation (NMT) training using document-level metrics with batch-level documents. Previous sequence-objective approaches to NMT training focus exclusively on sentence-level metrics like sentence BLEU which do not correspond to the desired evaluation metric, typically document BLEU. Meanwhile research into document-level NMT training focuses on data or model architecture rather than training procedure. We find that each of these lines of research has a clear space in it for the other, and propose merging them with a scheme that allows a document-level evaluation metric to be used in the NMT training objective. We first sample pseudo-documents from sentence samples. We then approximate the expected document BLEU gradient with Monte Carlo sampling for use as a cost function in Minimum Risk Training (MRT). This two-level sampling procedure gives NMT performance gains over sequence MRT and maximum-likelihood training. We demonstrate that training is more robust for document-level metrics than with sequence metrics. We further demonstrate improvements on NMT with TER and Grammatical Error Correction (GEC) using GLEU, both metrics used at the document level for evaluations.
