Sequence-to-Sequence Models for Automated Text Simplification [Konferenzbericht] Paper presented at the International Conference on Artificial Intelligence in Education (AIED) (21st, 2020).

Autor/inn/en	Botarleanu, Robert-Mihai; Dascalu, Mihai; Crossley, Scott Andrew; McNamara, Danielle S.
Titel	Sequence-to-Sequence Models for Automated Text Simplification [Konferenzbericht] Paper presented at the International Conference on Artificial Intelligence in Education (AIED) (21st, 2020).
Quelle	(2020), (8 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Natural Language Processing; Writing Skills; Difficulty Level; Reading Comprehension; Second Language Learning; Automation; Models; Computational Linguistics; Translation + Suchen Sie Ihr Suchwort? Natürliche Sprache; Writing skill; Schreibfertigkeit; Schwierigkeitsgrad; Leseverstehen; Zweitsprachenerwerb; Analogiemodell; Linguistics; Computerlinguistik
Abstract	A key writing skill is the capability to clearly convey desired meaning using available linguistic knowledge. Consequently, writers must select from a large array of idioms, vocabulary terms that are semantically equivalent, and discourse features that simultaneously reflect content and allow readers to grasp meaning. In many cases, a simplified version of a text is needed to ensure comprehension on the part of a targeted audience (e.g., second language learners). To address this need, we propose an automated method to simplify texts based on paraphrasing. Specifically, we explore the potential for a deep learning model, previously used for machine translation, to learn a simplified version of the English language within the context of short phrases. The best model, based on an Universal Transformer architecture, achieved a BLEU score of 66.01. We also evaluated this model's capability to perform similar transformation to texts that were simplified by human experts at different levels. [This work was also supported by a grant from the Romanian National Authority for Scientific Research and Innovation. This paper was published in: I. I. Bittencourt, et al (Eds.), "Proceedings of the 21st International Conference on Artificial Intelligence in Education" (AIED 2020). Lecture Notes in Artificial Intelligence (LNAI, Vol. 12164, pp.31-36). Springer, Cham.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01