Sebastian Stennmanns


Cross-Lingual Content Scoring
Andrea Horbach | Sebastian Stennmanns | Torsten Zesch
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

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.