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

Managing the Global Conversation

Joint Learning of Constituency and Dependency Grammars by Decomposed Cross-Lingual Induction

  • Posted: 30 Oct 2015
  • Author: Andy Way, Dave Lewis, Ankit Srivastava
  • Publication: MT Summit 2015
Journal Article

Explicitation, Unique Items and the Translation of English Passives in Thai Legal Texts

  • Posted: 1 Apr 2019
  • Author: , Dorothy Kenny , Mali Satthachai
  • Publication: Meta: Journal des traducteurs
Journal Article

La traduction automatique comme outil d’aide à la rédaction scientifique en anglais langue seconde : résultats d’une étude exploratoire sur la qualité linguistique

  • Posted: 1 Nov 2018
  • Author: Carla Parra Escartín, Sharon O’Brien, Goulet, Marie-Josee, Simard, Michel
  • Publication: Asp (Anglais de spécialité)
Book Chapter

Foreign Residents' Experiences of the Flyjin Phenomenon in the 2011 Great East Japan Earthquake

  • Posted: 3 Oct 2018
  • Author:
  • Publication: Crisis and Disaster in Japan and New Zealand (SPi Global)

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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