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

Referential Translation Machines for Predicting Translation Quality and Related Statistics

  • Posted: 16 Sep 2015
  • Author: Andy Way, Ergun Biici
  • Publication: EMNLP 2015 Tenth Workshop on Statistical Machine Translation
Transforming Global Content

Semantics-Enhanced Task-Oriented Dialogue Translation: A Case Study on Hotel Booking

  • Posted: 27 Nov 2017
  • Author: Andy Way, Liangyou Li
  • Publication: 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)
Conference

Extracting In-domain Training Data for Neural Machine Translation Using Data Selection Methods

  • Posted: 1 Nov 2018
  • Author: Chao-Hong Liu, Alberto Poncelas, Catarina Cruz Silva, Andy Way
  • Publication: WMT18 - 3rd Conference on Machine Translation
Book Chapter

Approaches to human and machine translation quality assessment

  • Posted: 13 Jul 2018
  • Author: Sheila Castilho, Federico Gaspari, Joss Moorkens, Stephen Doherty

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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Exciting times for our former colleague @petercahill and his company Voysis. Congratulations to Peter and all the V… twitter.com/i/web/status/1…

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