no code implementations • ACL (unimplicit) 2021 • Marie Bexte, Andrea Horbach, Torsten Zesch
We therefore quantify to what extent implicit language phenomena occur in short answer datasets and examine the influence they have on automatic scoring performance.
1 code implementation • NAACL (BEA) 2022 • Marie Bexte, Andrea Horbach, Torsten Zesch
The dominating paradigm for content scoring is to learn an instance-based model, i. e. to use lexical features derived from the learner answers themselves.
1 code implementation • NAACL (BEA) 2022 • Yuning Ding, Marie Bexte, Andrea Horbach
In this paper, we explore the role of topic information in student essays from an argument mining perspective.
1 code implementation • LREC 2022 • Marie Bexte, Ronja Laarmann-Quante, Andrea Horbach, Torsten Zesch
Spellchecking text written by language learners is especially challenging because errors made by learners differ both quantitatively and qualitatively from errors made by already proficient learners.
no code implementations • LREC 2020 • Andrea Horbach, Itziar Aldabe, Marie Bexte, Oier Lopez de Lacalle, Montse Maritxalar
Automatic generation of reading comprehension questions is a topic receiving growing interest in the NLP community, but there is currently no consensus on evaluation metrics and many approaches focus on linguistic quality only while ignoring the pedagogic value and appropriateness of questions.