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



Data Selection with Feature Decay Algorithms Using an Approximated Target Side

  • Posted: 1 Nov 2018
  • Author: Alberto Poncelas, Andy Way, Gideon Maillette de Buy Wenniger
  • Publication: IWSLT 2018 - 15th International Workshop on Spoken Language Translation
Book Chapter

Introduction. Translation Quality Assessment: From Principles to Practice

  • Posted: 1 Jul 2018
  • Author: Joss Moorkens, Federico Gaspari, Sheila Castilho, Stephen Doherty
  • Publication: Part of the Machine Translation: Technologies and Applications book series (MATRA, volume 1)
Journal Article

TermFinder: Log-Likelihood Comparison and Phrase-Based Statistical Machine Translation Models for Bilingual Terminology Extraction

  • Posted: 3 Feb 2019
  • Author: , Sergio Penkale, Rejwanul Haque and Andy Way
  • Publication: Language Resources and Evaluation

A Systematic Comparison Between SMT and NMT on Translating User-Generated Content

  • Posted: 15 Apr 2019
  • Author: Pintu Lohar, Maja Popović, Haithem Afli, Andy Way
  • Publication: CICLing 2019 - 20th International Conference on Computational Linguistics and Intelligent Text Processing

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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