Identifying the machine translation error types with the greatest impact on post-editing effort

Translation Environment Tools make translators' work easier by providing them with term lists, translation memories and machine translation output. Ideally, such tools automatically predict whether it is more effortful to post-edit than to translate from scratch, and determine whether or not to provide translators with machine translation output. Current machine translation quality estimation systems heavily rely on automatic metrics, even though they do not accurately capture actual post-editing effort. In addition, these systems do not take translator experience into account, even though novices' translation processes are different from those of professional translators. In this paper, we report on the impact of machine translation errors on various types of post-editing effort indicators, for professional translators as well as student translators. We compare the impact of MT quality on a product effort indicator (HTER) with that on various process effort indicators. The translation and post-editing process of student translators and professional translators was logged with a combination of keystroke logging and eye-tracking, and the MT output was analyzed with a fine-grained translation quality assessment approach. We find that most post-editing effort indicators (product as well as process) are influenced by machine translation quality, but that different error types affect different post-editing effort indicators, confirming that a more fine-grained MT quality analysis is needed to correctly estimate actual post-editing effort. Coherence, meaning shifts, and structural issues are shown to be good indicators of post-editing effort. The additional impact of experience on these interactions between MT quality and post-editing effort is smaller than expected

Improving the post-editing experience using translation recommendation: a user study

We report findings from a user study with professional post-editors using a translation recommendation framework (He et al., 2010) to integrate Statistical Machine Translation (SMT) output with Translation Memory (TM) systems. The framework recommends SMT outputs to a TM user when it predicts that SMT outputs are more suitable for post-editing than the hits provided by the TM. We analyze the effectiveness of the model as well as the reaction of potential users. Based on the performance statistics and the users’comments, we find that translation recommendation can reduce the workload of professional post-editors and improve the acceptance of MT in the localization industry

Translation methods and experience : a comparative analysis of human translation and post-editing with students and professional translators

While the benefits of using post-editing for technical texts have been more or less acknowledged, it remains unclear whether post-editing is a viable alternative to human translation for more general text types. In addition, we need a better understanding of both translation methods and how they are performed by students as well as professionals, so that pitfalls can be determined and translator training can be adapted accordingly. In this article, we aim to get a better understanding of the differences between human translation and post-editing for newspaper articles. Processes were registered by means of eye tracking and keystroke logging, which allows us to study translation speed, cognitive load, and the usage of external resources. We also look at the final quality of the product as well as translators' attitude towards both methods of translation

LIUM Machine Translation Systems for WMT17 News Translation Task

This paper describes LIUM submissions to WMT17 News Translation Task for English-German, English-Turkish, English-Czech and English-Latvian language pairs. We train BPE-based attentive Neural Machine Translation systems with and without factored outputs using the open source nmtpy framework. Competitive scores were obtained by ensembling various systems and exploiting the availability of target monolingual corpora for back-translation. The impact of back-translation quantity and quality is also analyzed for English-Turkish where our post-deadline submission surpassed the best entry by +1.6 BLEU.Comment: News Translation Task System Description paper for WMT1

The impact of machine translation error types on post-editing effort indicators

In this paper, we report on a post-editing study for general text types from English into Dutch conducted with master's students of translation. We used a fine-grained machine translation (MT) quality assessment method with error weights that correspond to severity levels and are related to cognitive load. Linear mixed effects models are applied to analyze the impact of MT quality on potential post-editing effort indicators. The impact of MT quality is evaluated on three different levels, each with an increasing granularity. We find that MT quality is a significant predictor of all different types of post-editing effort indicators and that different types of MT errors predict different post-editing effort indicators

Traducción de la Disposición 12792/2016 ANMAT: procedimiento para la solicitud de importación de la medicación/ tratamiento /materiales para el acceso post-estudio (selección, versión 1.0)

Translation from Spanish to English of ANMAT’s procedure for import of post-trial access provisions. This is a regulatory mechanisim to comply with post-trial provisions requirement in Declaration of Helsinki, paragraph 34. The translation it is based on a selection of the text of ANMAT’s Provision 12792/2016

A Shared Task on Bandit Learning for Machine Translation

We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine translation from weak user feedback instead of human references or post-edits. On each of a sequence of rounds, a machine translation system is required to propose a translation for an input, and receives a real-valued estimate of the quality of the proposed translation for learning. This paper describes the shared task's learning and evaluation setup, using services hosted on Amazon Web Services (AWS), the data and evaluation metrics, and the results of various machine translation architectures and learning protocols.Comment: Conference on Machine Translation (WMT) 201