Contextual Definition Generation [Konferenzbericht] Paper presented at the International Workshop on Intelligent Textbooks (3rd, 2021).

Autor/inn/en	Yarbro, Jeffrey T.; Olney, Andrew M.
Titel	Contextual Definition Generation [Konferenzbericht] Paper presented at the International Workshop on Intelligent Textbooks (3rd, 2021).
Quelle	(2021), (10 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Definitions; Learning Processes; Models; Context Effect; Evaluators; Sentences; Computational Linguistics; Accuracy; Textbooks; Vocabulary Development; Collaborative Writing; Multiple Choice Tests; Higher Education; Computer Software; Interrater Reliability; Editing; Web 2.0 Technologies; Web Sites + Suchen Sie Ihr Suchwort? Begriffsbestimmung; Learning process; Lernprozess; Analogiemodell; Sentence analysis; Satzanalyse; Linguistics; Computerlinguistik; Textbook; Text book; Schulbuch; Lehrbuch; Wortschatzarbeit; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Hochschulbildung; Hochschulsystem; Hochschulwesen; Interrater-Reliabilität; Redaktion; Textbearbeitung; Web-Design
Abstract	This paper explores the concept of dynamically generating definitions using a deep-learning model. We do this by creating a dataset that contains definition entries and contexts associated with each definition. We then fine-tune a GPT-2 based model on the dataset to allow the model to generate contextual definitions. We evaluate our model with human raters by generating definitions using two context types: short-form (the word used in a sentence) and long-form (the word used in a sentence along with the prior and following sentences). Results indicate that the model performed significantly better when generating definitions using short-form contexts. Additionally, we evaluate our model against human-generated definitions. The results show promise for the model, showing that the model was able to match human-level fluency. However, while it was able to reach human-level accuracy in some instances, it failed in others. [This paper was published in: "Proceedings of the Third International Workshop on Intelligent Textbooks," Vol. 2895, CEUR-WS.org, 2021, pp. 74-83.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01