Identifying Feedback Types to Augment Feedback Comment Generation

verfasst von

Maja Stahl, Henning Wachsmuth

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.

Organisationseinheit(en)

Fachgebiet Maschinelle Sprachverarbeitung

Typ

Aufsatz in Konferenzband

Seiten

31-36

Publikationsdatum

09.2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

Ziele für nachhaltige Entwicklung

SDG 4 – Qualitativ hochwertige Bildung

Elektronische Version(en)

https://aclanthology.org/2023.inlg-genchal.5 (Zugang: Offen)