论文标题
数据到文本生成的机器翻译预培训 - 捷克的案例研究
Machine Translation Pre-training for Data-to-Text Generation -- A Case Study in Czech
论文作者
论文摘要
虽然有大量的研究研究从结构化数据中生成文本的深度学习方法，但几乎所有这些都纯粹集中在英语上。在本文中，我们研究了基于机器翻译的预培训对非英语语言的数据到文本生成的有效性。由于结构化数据通常以英语表示，因此文本生成涉及翻译，音译和复制的元素 - 已经在神经机器翻译系统中编码的元素。此外，由于数据到文本语料库通常很小，因此此任务可以从预培训中受益匪浅。根据我们对形态上复杂的语言捷克的实验，我们发现训练预训练使我们能够以自动指标和人类评估来判断具有显着提高性能的端到端模型。我们还表明，这种方法享有几种理想的属性，包括在低数据方案中的性能提高以及可稳健的插槽值。
While there is a large body of research studying deep learning methods for text generation from structured data, almost all of it focuses purely on English. In this paper, we study the effectiveness of machine translation based pre-training for data-to-text generation in non-English languages. Since the structured data is generally expressed in English, text generation into other languages involves elements of translation, transliteration and copying - elements already encoded in neural machine translation systems. Moreover, since data-to-text corpora are typically small, this task can benefit greatly from pre-training. Based on our experiments on Czech, a morphologically complex language, we find that pre-training lets us train end-to-end models with significantly improved performance, as judged by automatic metrics and human evaluation. We also show that this approach enjoys several desirable properties, including improved performance in low data scenarios and robustness to unseen slot values.
