Identifying Feedback Types to Augment Feedback Comment Generation
Abstract
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.
Details
- Organisationseinheit(en)
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Fachgebiet Maschinelle Sprachverarbeitung
- Typ
- Aufsatz in Konferenzband
- Seiten
- 31-36
- Anzahl der Seiten
- 6
- Publikationsdatum
- 09.2023
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Theoretische Informatik und Mathematik, Angewandte Informatik, Information systems, Software
- Ziele für nachhaltige Entwicklung
- SDG 4 - Qualitativ hochwertige Bildung
- Elektronische Version(en)
-
https://doi.org/10.18653/v1/2023.inlg-genchal.5 (Zugang:
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