eSCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing
Abstract
Training models for the automatic correction of machine-translated text usually relies on data consisting of (source, MT, human_post-edit) triplets providing, for each source sentence, examples of translation errors with the corresponding corrections made by a human post-editor. Ideally, a large amount of data of this kind should allow the model to learn reliable correction patterns and effectively apply them at test stage on unseen (source, MT) pairs. In practice, however, their limited availability calls for solutions that also integrate in the training process other sources of knowledge. Along this direction, state-of-the-art results have been recently achieved by systems that, in addition to a limited amount of available training data, exploit artificial corpora that approximate elements of the “gold” training instances with automatic translations. Following this idea, we present eSCAPE, the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. eSCAPE consists of millions of entries in which the MT element of the training triplets has been obtained by translating the source side of publicly-available parallel corpora, and using the target side as
中文速览
自动后编辑（Automatic Post-Editing, APE）旨在让模型自动修正机器翻译的错误，但训练这类模型需要大量"源文本—机器译文—人工修改版"三元组数据，而现实中这类数据极为稀缺。为此，研究者们提出用合成数据来补充训练，本文介绍的 eSCAPE 正是目前规模最大的免费开放合成语料库：通过对公开平行语料的源端分别用短语和神经机器翻译模型自动翻译，再以目标端作为"人工修改版"替代，为英德和英意两个方向各构建了逾千万条三元组。在最具挑战性的通用领域场景下的实验表明，仅用 eSCAPE 训练的模型就能在两个语言方向上显著提升机器翻译质量。这一资源的公开发布填补了 APE 研究中大规模训练数据的空白，有望推动神经自动后编辑系统的进一步发展。
原文 arXiv:1803.07274;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1803.07274v1