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


Book Chapter

Machine Translation

  • Posted: 4 Oct 2018
  • Author: , Dorothy Kenny
  • Publication: The Routledge Handbook of Translation and Philosophy
Transforming Global Content

Comparing Unsupervised and Rule-based Morphological Segmentation in Statistical Machine Translation

  • Posted: 3 Jun 2015
  • Author: Andy Way, Tommi Pirinen, Raphael Rubino, Antonio Toral
  • Publication: MTSUMMIT 2015

Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates

  • Posted: 1 Jun 2018
  • Author: Mohammed Hasanuzzaman, Andy Way, Kamila Sabyasachi, Asif Ekbal, Pushpak Bhattacharyya
  • Publication: NAACL HLT 2018 - 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human La
Transforming Global Content

Ethical Considerations in NLP Shared Tasks

  • Posted: 4 Apr 2017
  • Author: Carla Parra Escartín, Joss Moorkens, Andy Way, Wessel Reijers, Teresa Lynn, Chao-Hong Liu
  • Publication: EthNLP 2017 - First ACL Workshop on Ethics in Natural Language 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).

11 am
“The difference between dreams and reality is execution” Prof Mark Ferguson ⁦@scienceirel⁩ ⁦@ambercentre⁩ ⁦……


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