Laboratory of Computer and Information Science / Neural Networks Research Centre CIS Lab Helsinki University of Technology

Learning to Translate

Reaching good quality machine translation (MT) is difficult and the development of a traditional MT system requires a lot of human effort. However, the availability of large corpora makes it possible to use various probabilistic and statistical methods to let the system generate the necessary resources automatically.

Within-language translation

In addition to the traditional translation from one language to another, we develop methods that enable translations or interpretations within one language.

The underpinning idea is the fact that contextual, experiential and/or disciplinary diversity impede interpersonal communication and understanding. Therefore, even speakers of one language may need translation tools that facilitate efficient communication, for example, between representatives of different professional domains.


Our approach is extensively based on unsupervised statistical machine learning techniques, including independent component analysis, self-organizing map, clustering, expectation maximization algorithm, compression and Bayesian methods. We also want to take carefully into account the underlying cognitive, linguistic and philosophical issues in order to avoid local minima in the technology development.


In 2006, we organized a Finnish-Swedish Machine Translation Challenge with our collaborators from the University of Helsinki. During autumn 2005 and early 2006 we organized a seminar on statistical machine translation.


Please, see also the full list of publications.


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