The Role of Context information in L2 Translation Assistance (Data Set)
Maarten van Gompel and Antal van den Bosch

Type: Dataset
Tags: machine translation, translation assistance, nlp, computational linguistics, semeval

title = {The Role of Context information in L2 Translation Assistance (Data Set)},
journal = {Language Resources and Evaluation (submitted, pending acceptation)},
author = {Maarten van Gompel and Antal van den Bosch},
year = {2014},
url = {},
license = {CC-SA},
abstract = {We investigate to what extent L2 context information can aid the
  translation of L1 fragments in an L2 context, and what techniques
  are most suitable. The task is framed in the context of second
  language learning, where translation assistance systems enable
  language learners to write in their target language whilst allowing
  them to fall back to their native language in case the correct word
  or expression is not known. These code switches are subsequently
  translated to L2 given the L2 context. We focus on two approaches: a
  classifier-based approach, and one rooted in Statistical Machine
  Translation. Various mixtures between the two are investigated. In
  doing so, we provide valuable insights on how to best tackle the
  task presented at SemEval 2014.  We zoom in on the role of context
  information (in L2) and of the L2 language model, and investigate
  the incorporation of memory-based classifiers as a means of better
  disambiguating the L1 fragments. We find Statistical Machine
  Translation to be the most adequate solution to the problem, and
  show how it can be applied with a cross-lingual context.
  Integrating classifiers in such a framework may lead to small
  improvements in translation quality, but there is considerable
  overlap with the benefits of the L2 language model.

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