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

Publications

Transforming Global Content

A Crowd-sourcing Approach for Translations of Minority Language User-Generated Content

  • Posted: 1 Jul 2017
  • Author: Andy Way, Meghan Dowling, Teresa Lynn
  • Publication: 1st Workshop on Social MT
Transforming Global Content

A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis

  • Posted: 30 Nov 2017
  • Author: Haithem Afli, Andy Way, Pintu Lohar, Koel Dutta Chowdhury, Mohammed Hasanuzzaman
  • Publication: The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)
Journal Article

Training Citizen Translators: Design and Delivery of Bespoke Training on the Fundamentals of Translation for New Zealand Red Cross

  • Posted: 1 Aug 2018
  • Author: Patrick Cadwell, Federico M. Federici
  • Publication: Translation Spaces
Conference

Improving Machine Translation of Educational Content via Crowdsourcing

  • Posted: 7 May 2018
  • Author: Sheila Castilho, Federico Gaspari, Maximiliana Behnke, Antonio Valerio, Miceli Barone, Rico Sennrich, Vilelmini Sosoni, Thanasis Naskos, Eirini Takoulidou, Maria Stasimioti, Menno Van Zaanen, Panayota Georgakopoulou, Valia Kordoni, Markus Egg, Katia Lida Kermanidis
  • Publication: LREC 2018 - 11th International Conference on Language Resources and Evaluation

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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