Automated Topical Component Extraction Using Neural Network Attention Scores from Source-Based Essay Scoring [Konferenzbericht] Paper presented at the Annual Meeting of the Association for Computational Linguistics (58th, Jul 5-10, 2020).

Autor/inn/en	Zhang, Haoran; Litman, Diane
Titel	Automated Topical Component Extraction Using Neural Network Attention Scores from Source-Based Essay Scoring [Konferenzbericht] Paper presented at the Annual Meeting of the Association for Computational Linguistics (58th, Jul 5-10, 2020).
Quelle	(2020), (16 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Computer Assisted Testing; Scoring; Essay Tests; Writing Evaluation; Grading; Formative Evaluation; Attention; Scoring Rubrics + Suchen Sie Ihr Suchwort? Bewertung; Schriftlicher Sprachgebrauch; Notengebung; Schulnote; Aufmerksamkeit; Scoring formulas; Auswertungsbogen
Abstract	While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays. [This paper was published in: "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics" (pp. 8569-8584). Association for Computational Linguistics.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01