Transforming Global Content

Transforming Global Content

Projects on Machine Translation (MT) modelling, MT training data scarcity and human factors are pivotal to extending research in MT, human translation and their business impact.

New deep learning techniques are being augmented with linguistic knowledge to constrain the MT decoding space explosion due to increasing model complexity. Cloud-based models seed MT engines built on-the-fly using small amounts of data targeted to the translational requirements of the input document. We extend our previous research on domain adaptation to new ADAPT sectors and data types using grounding semantics, filtering out ‘noisy’ input, and where data is in short supply, supplement parallel training data with comparable corpora. We also extend our previous ethnographic studies of real users of MT output, which will uncover cognitive and social barriers to MT acceptability. Novel evaluation schemes are being developed which meet industry needs for flexible, configurable quality measures that reflect directly their core organisational goals.

Research team



What is the impact of raw MT on Japanese users of Word: Preliminary results of a usability study using eye-tracking

  • Posted: 23 Aug 2019
  • Author: Ana Guerberof-Arenas, Joss Moorkens, Sharon O’Brien
  • Publication: MT Summit XVII: the 17th Machine Translation Summit
Transforming Global Content

Using Images to Improve Machine-Translating E-Commerce Product Listings

  • Posted: 1 Apr 2017
  • Author: Sheila Castilho, Andy Way, Daniel Stein, Evgeny Matusov
  • Publication: EACL 2017 - 15th Conference of the European Chapter of the Association for Computational Linguistics
Journal Article

Language, Culture and Translation in Disaster ICT, An Ecosystemic Model of Understanding

  • Posted: 1 Aug 2016
  • Author: Sharon O’Brien
  • Publication: Perspectives: Studies in Translatology
Journal Article

Special Issue: Human Factors in Neural Machine Translation

  • Posted: 1 Jun 2019
  • Author: Sheila Castilho, Federico Gaspari, Joss Moorkens, Maja Popović, Antonio Toral (Editors)
  • Publication: Machine Translation

Research Goals

We provide MT with increased intelligence, by developing engines incorporating syntax, semantics and discourse features, constrained MT models using deep learning techniques, cloud-based data models for use by (disposable) MT engines, and engines for sentiment analysis and translation.

We connect texts with the real world, and investigate different ways to leverage grounding semantics (in contrast to abstract semantics), including named entities and relations, multimodality, and discourse semantics to improve translation quality in various scenarios. We use the state-of-the-art neural MT framework to incorporate grounding semantics and rich linguistic features.

Through a human-factors oriented approach, we seek to understand what the blocking points are, in order to overcome them. We take a cognitive ergonomic approach to this, which investigates three types of factors: cognitive (i.e. best presentation), physical (i.e. reduced editing effort), and organisational (i.e. best organisation for the adoption of MT).

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