Sequence tagging in EFL Email texts as feedback for language learners
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Publikationsdaten
| Von | Yuning Ding, Ruth Trüb, Johanna Fleckenstein, Stefan Keller, Andrea Horbach |
| Originalsprache | Englisch |
| Erschienen in | Proceedings of the 12th Workshop on NLP for Computer Assisted Language Learning: (NLP4CALL 2023) |
| Seiten | 53-62 |
| Herausgeber (Verlag) | LiU Electronic Press |
| DOI/Link | https://aclanthology.org/2023.nlp4call-1.7/ |
| Publikationsstatus | Veröffentlicht – 2023 |
| Keywords | open-access, peer-reviewed, NG-EduNLP |
When predicting scores for different aspects of a learner text, automated scoring algorithms usually cannot provide information about which part of text a score is referring to. We therefore propose a method to automatically segment learner texts as a way towards providing visual feedback. We train a neural sequence tagging model and use it to segment EFL email texts into functional segments. Our algorithm reaches a token-based accuracy of 90% when trained per prompt and between 83 and 87% in a cross-prompt scenario.
