论文标题
多编码器有帮助吗？关于上下文感知神经机器翻译的案例研究
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation
论文作者
论文摘要
在编码器 - 模型模型中，除了单个句子外，多个编码器通常用于表示上下文信息。在本文中，我们研究了DocordLevel神经机器翻译（NMT）中的多编码方法。令人惊讶的是，我们发现上下文编码器不仅编码周围的句子，而且表现为噪声发生器。这使我们重新考虑了在上下文感知翻译中多编码器的真正好处 - 一些改进来自强大的培训。我们比较了几种将噪声和/或调整良好的辍学设置引入这些编码器的训练的方法。实验结果表明，嘈杂的训练在基于多编码器的NMT中起着重要作用，尤其是在训练数据很小的情况下。此外，我们通过仔细使用噪声和辍学方法来建立在IWSLT FR-EN任务上的新最新。
In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence. In this paper, we investigate multi-encoder approaches in documentlevel neural machine translation (NMT). Surprisingly, we find that the context encoder does not only encode the surrounding sentences but also behaves as a noise generator. This makes us rethink the real benefits of multi-encoder in context-aware translation - some of the improvements come from robust training. We compare several methods that introduce noise and/or well-tuned dropout setup into the training of these encoders. Experimental results show that noisy training plays an important role in multi-encoder-based NMT, especially when the training data is small. Also, we establish a new state-of-the-art on IWSLT Fr-En task by careful use of noise generation and dropout methods.
