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.- Anthology ID:
- W18-0550
- Volume:
- Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Joel Tetreault, Jill Burstein, Ekaterina Kochmar, Claudia Leacock, Helen Yannakoudakis
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 410–419
- Language:
- URL:
- https://aclanthology.org/W18-0550
- DOI:
- 10.18653/v1/W18-0550
- Cite (ACL):
- Andrea Horbach, Sebastian Stennmanns, and Torsten Zesch. 2018. Cross-Lingual Content Scoring. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 410–419, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Content Scoring (Horbach et al., BEA 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W18-0550.pdf