no code implementations • NAACL (BEA) 2022 • Ronja Laarmann-Quante, Leska Schwarz, Andrea Horbach, Torsten Zesch
When listening comprehension is tested as a free-text production task, a challenge for scoring the answers is the resulting wide range of spelling variants.
no code implementations • EACL (BEA) 2021 • Christian Haring, Rene Lehmann, Andrea Horbach, Torsten Zesch
We present the C-Test Collector, a web-based tool that allows language learners to test their proficiency level using c-tests.
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 • Asian Chapter of the Association for Computational Linguistics 2020 • Yuning Ding, Andrea Horbach, Torsten Zesch
As a review of prior work for Chinese content scoring shows a lack of open-access data in the field, we present two short-answer data sets for Chinese.
no code implementations • COLING 2020 • Yuning Ding, Brian Riordan, Andrea Horbach, Aoife Cahill, Torsten Zesch
Automatic content scoring systems are widely used on short answer tasks to save human effort.
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.
no code implementations • WS 2018 • Andrea Horbach, Sebastian Stennmanns, Torsten Zesch
We investigate the feasibility of cross-lingual content scoring, a scenario where training and test data in an automatic scoring task are from two different languages.
no code implementations • WS 2017 • Andrea Horbach, Yuning Ding, Torsten Zesch
Spelling errors occur frequently in educational settings, but their influence on automatic scoring is largely unknown.
no code implementations • WS 2017 • Brian Riordan, Andrea Horbach, Aoife Cahill, Torsten Zesch, Chong MIn Lee
Neural approaches to automated essay scoring have recently shown state-of-the-art performance.
no code implementations • WS 2017 • Andrea Horbach, Dirk Scholten-Akoun, Yuning Ding, Torsten Zesch
Automatic essay scoring is nowadays successfully used even in high-stakes tests, but this is mainly limited to holistic scoring of learner essays.
no code implementations • LREC 2016 • Lena Keiper, Andrea Horbach, Stefan Thater
We present a novel method to automatically improve the accurracy of part-of-speech taggers on learner language.
no code implementations • LREC 2016 • Andrea Horbach, Andrea Hensler, Sabine Krome, Jakob Prange, Werner Scholze-Stubenrecht, Diana Steffen, Stefan Thater, Christian Wellner, Manfred Pinkal
We present an annotation study on a representative dataset of literal and idiomatic uses of German infinitive-verb compounds in newspaper and journal texts.
no code implementations • LREC 2016 • Stefan Ecker, Andrea Horbach, Stefan Thater
We propose an unsupervised system for a variant of cross-lingual lexical substitution (CLLS) to be used in a reading scenario in computer-assisted language learning (CALL), in which single-word translations provided by a dictionary are ranked according to their appropriateness in context.
no code implementations • LREC 2014 • Andrea Horbach, Alexis Palmer, Magdalena Wolska
n this paper we investigate the potential of answer clustering for semi-automatic scoring of short answer questions for German as a foreign language.