Nearest Neighbor Non-autoregressive Text Generation

Ayana Niwa, Sho Takase, Naoaki Okazaki
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引用次数: 5

Abstract

Non-autoregressive (NAR) models can generate sentences with less computation than autoregressive models but sacrifice generation quality. Previous studies addressed this issue through iterative decoding. This study proposes using nearest neighbors as the initial state of an NAR decoder and editing them iteratively. We present a novel training strategy to learn the edit operations on neighbors to improve NAR text generation. Experimental results show that the proposed method (NeighborEdit) achieves higher translation quality (1.69 points higher than the vanilla Transformer) with fewer decoding iterations (one-eighteenth fewer iterations) on the JRC-Acquis En-De dataset, the common benchmark dataset for machine translation using nearest neighbors. We also confirm the effectiveness of the proposed method on a data-to-text task (WikiBio). In addition, the proposed method outperforms an NAR baseline on the WMT'14 En-De dataset. We also report analysis on neighbor examples used in the proposed method.
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最近邻非自回归文本生成
与自回归模型相比,非自回归模型能以更少的计算量生成句子,但会牺牲生成质量。以前的研究通过迭代解码解决了这个问题。本研究提出使用最近邻作为NAR解码器的初始状态,并对其进行迭代编辑。我们提出了一种新的训练策略来学习邻居的编辑操作,以提高NAR文本的生成。实验结果表明,本文提出的方法(neighborredit)在JRC-Acquis En-De数据集(使用最近邻进行机器翻译的常用基准数据集)上以更少的解码迭代(迭代次数减少1 / 18)获得了更高的翻译质量(比vanilla Transformer高1.69分)。我们还证实了所提出的方法在数据到文本任务(WikiBio)上的有效性。此外,该方法在WMT'14 En-De数据集上优于NAR基线。我们还报道了对所提出方法中使用的邻例的分析。
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