Online Neural Automatic Post-editing for Neural Machine Translation
Authors: Matteo Negri, Marco Turchi, Nicola Bertoldi, Marcello Federico (Fondazione Bruno Kessler, MMT srl, 2018)
Topic: Machine Translation

Abstract
Machine learning from user corrections is key to the industrial deployment of machine translation (MT). We introduce the ﬁrst on-line approach to automatic post-editing (APE), i.e. the task of automatically correcting MT errors. We present experimental results of APE on English-Italian MT by simulating human post-edits with human reference translations, and by applying online APE on MT outputs of increasing quality. By evaluating APE on generic vs. specialised and static vs. adaptive neural MT, we address the question: At what cost on the MT side will APE become useless?

Introduction
Automatic Post-editing for MT is a supervised learning task aimed to correct errors in a machinetranslated text (Knight and Chander, 1994; Simard et al., 2007). Cast as a problem of “monolingual translation” (from raw MT output into improved text in the same target language), APE has followed a similar evolution to that of MT. As in MT, APE research received a strong boost from shared evaluation exercises like those organized within the well-established WMT Conference on Machine Translation (Chatterjee et al., 2018). In terms of approaches, early MT-like phrase-based solutions (B´echara et al., 2011; Rosa et al., 2013; Lagarda et al., 2015; Chatterjee et al., 2015) have been recently outperformed and replaced by neural architectures that now represent the state of the art (Junczys-Dowmunt and Grundkiewicz, 2016; Chatterjee et al., 2017a; Tebbifakhr et al., 2018; Junczys-Dowmunt and Grundkiewicz, 2018). From the industry standpoint, APE has started to attract MT market players interested in combining the two technologies to support human translation in professional workﬂows (Crego et al., 2016). Focusing on this industry-oriented perspective, this paper makes a step further on APE research by exploring an online neural approach to the task. The goal is to leverage human feedback (post edits) to improve on-the-ﬂy a neural APE model without the need of stopping it for ﬁnetuning or re-training from scratch. Online learning capabilities are crucial (both for APE and MT) in computer-assisted translation scenarios where professional translators operate on suggestions provided by machines. In such scenarios, human corrections represent an invaluable source of knowledge that systems should exploit to enhance users’ experience and increase their productivity. Towards these objectives we provide two contributions. One is the ﬁrst online approach to neural APE. Indeed, while MT-like online learning techniques have been proposed for phrase-based APE (Ortiz-Mart´ınez and Casacuberta, 2014; Simard and Foster, 2013; Chatterjee et al., 2017b), nothing has been done yet under the state-of-the-art neural paradigm. In doing this, the other contribution is the ﬁrst evaluation of neural APE run on the output of neural MT (NMT). So far, published results report signiﬁcant gains1 when APE is run to correct the output of a phrase-based MT system. To our knowledge, the true potential of APE with higher quality NMT output has not been investigated yet. The last observation introduces a more general discussion on the relation between MT and APE. Since, by deﬁnition, APE’s reason of being is the sub-optimal quality of MT output, one might wonder if the level of current MT technology still justiﬁes efforts on APE. Along this direction, our third contribution is an analysis of online neural APE applied to the output of NMT systems featuring different levels of performance. Our competitors range from a generic model trained on large parallel data (mimicking the typical scenario in which industry users – e.g. Language Service Providers – rely on web-based services or other black-box systems) to highly customized online models (like those that LSPs would desire but typically cannot afford). Our experiments in this range of conditions aim to shed light on the future of APE from the industry standpoint by answering the question: At what cost on the MT side will APE become useless?

Conclusion
We introduced an online neural APE system, which is trained on generic data and only exploits user feedback to improve its performance, and evaluated it on the output of NMT systems featuring increasing complexity and in-domain data demand. Our results show the effectiveness of current APE technology in the typical setting of 4Statistical signiﬁcance is computed with paired bootstrap resampling (Koehn, 2004).