Cross-Lingual Content Scoring

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 reusing trained models from other languages. As there is nosuitable dataset available for this new task, wecreate 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.

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Conference:
NAACL HLT 2018, Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, New Orleans, Louisiana, June 5, 2018
Date Issued:
2018
URN:
urn:nbn:de:hbz:464-20211019-110432-4
DOI:
10.18653/v1/W18-0550
Language:
English
Type of Resource:
Text
Collection:
E-Publications
Subject categories of the Deutsche Nationalbibliografie:
004 Data processing Computer sciences
Hosting institution:
Faculty of Engineering, Computer Science and Applied Cognitive Science, Computer Science, Language Technology Lab
Info on primary publication:
Horbach, A., Stennmanns, S., Zesch, T. (2018). Cross-Lingual Content Scoring. In: Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 410-419. Association for Computational Linguistics. DOI: https://doi.org/10.18653/v1/W18-0550

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