Summary of the paper

Title ESCRITO - An NLP-Enhanced Educational Scoring Toolkit
Authors Torsten Zesch and Andrea Horbach
Abstract We propose Escrito, a toolkit for scoring student writings using NLP techniques that addresses two main user groups: teachers and NLP researchers. Teachers can use a high-level API in the teacher mode to assemble scoring pipelines easily. NLP researchers can use the developer mode to access a low-level API, which not only makes available a number of pre-implemented components, but also allows the user to integrate their own readers, preprocessing components, or feature extractors. In this way, the toolkit provides a ready-made testbed for applying the latest developments from NLP areas like text similarity, paraphrase detection, textual entailment, and argument mining within the highly challenging task of educational scoring and feedback. At the same time, it allows teachers to apply cutting-edge technology in the classroom.
Topics Tools, Systems, Applications, Other, Computer-Assisted Language Learning (Call)
Full paper ESCRITO - An NLP-Enhanced Educational Scoring Toolkit
Bibtex @InProceedings{ZESCH18.590,
  author = {Torsten Zesch and Andrea Horbach},
  title = "{ESCRITO - An NLP-Enhanced Educational Scoring Toolkit}",
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {May 7-12, 2018},
  address = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {979-10-95546-00-9},
  language = {english}
Powered by ELDA © 2018 ELDA/ELRA