@InProceedings{horbach-stennmanns-zesch:2018:W18-05,
  author    = {Horbach, Andrea  and  Stennmanns, Sebastian  and  Zesch, Torsten},
  title     = {Cross-Lingual Content Scoring},
  booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {410--419},
  abstract  = {We investigate the feasibility of cross-lingual content scoring, a scenario where training and test data in an automatic scoring task are from two different languages. Cross-lingual scoring can contribute to educational equality by allowing answers in multiple languages. Training a model in one language and applying it to another language might also help to overcome data sparsity issues by re-using trained models from other languages. As there is no suitable dataset available for this new task, we create a comparable bi-lingual corpus by extending the English ASAP dataset with German answers. Our experiments with cross-lingual scoring based on machine-translating either training or test data show a considerable drop in scoring quality.},
  url       = {http://www.aclweb.org/anthology/W18-0550}
}