Cross-Lingual Content Scoring
Andrea Horbach, Sebastian Stennmanns, Torsten Zesch · 2018
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 crosslingual scoring based on machine-translating either training or test data show a considerable drop in scoring quality.