Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges |
| Seiten | 31-36 |
| Publikationsstatus | Veröffentlicht - Sept. 2023 |
Abstract
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Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges. 2023. S. 31-36.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Identifying Feedback Types to Augment Feedback Comment Generation
AU - Stahl, Maja
AU - Wachsmuth, Henning
PY - 2023/9
Y1 - 2023/9
N2 - In the context of language learning, feedback comment generation is the task of generating hints or explanatory notes for learner texts that help understand why a part of text is erroneous. This paper presents our approach to the Feedback Comment Generation Shared Task, collocated with the 16th International Natural Language Generation Conference (INLG 2023). The approach augments the generation of feedback comments by a self-supervised identification of feedback types in a multitask-learning setting. Within the shared task, other approaches performed more effective, yet the combined modeling of feedback type classification and feedback comment generation is superior to performing feedback generation only.
AB - In the context of language learning, feedback comment generation is the task of generating hints or explanatory notes for learner texts that help understand why a part of text is erroneous. This paper presents our approach to the Feedback Comment Generation Shared Task, collocated with the 16th International Natural Language Generation Conference (INLG 2023). The approach augments the generation of feedback comments by a self-supervised identification of feedback types in a multitask-learning setting. Within the shared task, other approaches performed more effective, yet the combined modeling of feedback type classification and feedback comment generation is superior to performing feedback generation only.
M3 - Conference contribution
SP - 31
EP - 36
BT - Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges
ER -