This project aimed to improve products and processes related to translators' interaction with technology, examine the efficacy of various working methods, and assess new training approaches for the translation workplace. With a focus on empowering translators to use machine translation effectively and boost job satisfaction, the project sought to raise professional standards and contribute to the multilingual economy. The deposit contains data for two studies where English-to-Chinese translators used neural machine translation (MT) to translate science fiction short stories in Trados Studio. One of the studies (t-p) compares post-editing to a ‘no MT’ condition. The other (segmentation) examines two ways of presenting the texts on screen for post-editing, namely by segmenting them into paragraphs or into sentences. The dataset is licenced under a Creative Commons "CC BY-NC-SA 2.5" license.Recent annual figures show that language services are a US$46-billion industry with high growth forecasts. Technology is crucial for coping with demand in this sector. In many contexts, translators now edit and interact with machine translation (i.e. systems akin to Google Translate) instead of translating texts from scratch. The productivity benefits of using machine translation in this way are well known, but the process is controversial and far from smooth. Attitudes to machine translation among language professionals are famously negative. In the press, translators are said to 'have the blues' (The Economist 2017) and to be 'concerned' (El Pais 2017) about the impact of machine translation on their jobs and work. Methods for training translators to use technology are often ineffective, and knowledge of how machine translation affects translators' linguistic decisions is still limited, which poses obstacles to a beneficial use of machine translation in professional workflows. This project is a joint effort to address these issues and improve products and processes linked to translators' interaction with technology. We will examine the efficacy of different working methods and assess new training approaches that can be deployed in the translation workplace. In a context of heated debates on the impact of automation on human labour, our aim is ultimately to empower translators to adopt machine translation in more effective and rewarding ways that can improve their job satisfaction, raise professional standards and boost the multilingual economy. The project will have three phases. In the first phase, we will investigate the use of neural machine translation, a new paradigm in machine translation technology, and assess different strategies that translators can adopt to improve their work. The second phase will delve deeper into links between translating behaviour and the quality of translated texts. We will examine these links across languages in the largest-scale study to date to establish a connection between what translators do and the quality of the texts they produce. In an innovative integration of research methods and business practices, the third phase of the project will examine how tools that track translators' activity (e.g. edits and translating speed) can be used positively and responsibly in the workplace. We will monitor translators' activity for four months to shed new light on issues such as data ethics and perceptions of productivity as well as on the practical implications of incorporating methods from the project's initial phases into commercial contexts. Throughout the project, we will use a unique combination of methodologies including objective and subjective techniques. The work is novel in several ways. First, it will provide best-practice documentation on the use of activity tracking in commercial settings and on key editing procedures so far ignored by industry standards and guidelines. Second, with a diverse team of researchers and industry partners, the project constitutes a rare collaboration between translators, academics, technology developers, translation businesses and professional bodies. Our work is rooted in problems reported by and discussed directly with our professional partners. Finally, we will veer away from previous initiatives focused predominantly on the development of new tools to represent a much-needed investment in know-how and capacity building. To date, translators' concerns about the profession have not necessarily aligned with top-down investments in technology that often disregard the technology users, with potentially negative consequences for the technology developers and for translators' professional standing. We hope to change this by concentrating on the human aspects of technological progress and providing empirical evidence on new ways of working and the efficacy of different professional practices.

English-to-Chinese translators used neural machine translation (MT) to translate science fiction short stories in Trados Studio.