no code implementations • ACL (WOAH) 2021 • Piush Aggarwal, Michelle Espranita Liman, Darina Gold, Torsten Zesch
This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021.
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 • NAACL (TeachingNLP) 2021 • Annemarie Friedrich, Torsten Zesch
It is generally agreed upon in the natural language processing (NLP) community that ethics should be integrated into any curriculum.
no code implementations • COLING (WNUT) 2022 • Piush Aggarwal, Torsten Zesch
Hate speech detection systems have been shown to be vulnerable against obfuscation attacks, where a potential hater tries to circumvent detection by deliberately introducing noise in their posts.
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 • NAACL (WOAH) 2022 • Florian Ludwig, Klara Dolos, Torsten Zesch, Eleanor Hobley
Despite recent advances in machine learning based hate speech detection, classifiers still struggle with generalizing knowledge to out-of-domain data samples.
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.
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.
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.
no code implementations • 27 Jun 2024 • Torsten Zesch, Michael Hanses, Niels Seidel, Piush Aggarwal, Dirk Veiel, Claudia de Witt
Using the full potential of LLMs in higher education is hindered by challenges with access to LLMs.
no code implementations • 8 Apr 2024 • Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer, Kamyar Arzideh, Giulia Baldini, Jan Trienes, Max Hasin, Jeanette Bewersdorff, Cynthia S. Schmidt, Marie Bauer, Kaleb E. Smith, Jiang Bian, Yonghui Wu, Jörg Schlötterer, Torsten Zesch, Peter A. Horn, Christin Seifert, Felix Nensa, Jens Kleesiek, Christoph M. Friedrich
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa.
no code implementations • 7 Feb 2024 • Piush Aggarwal, Jawar Mehrabanian, Weigang Huang, Özge Alacam, Torsten Zesch
This paper delves into the formidable challenge of cross-domain generalization in multimodal hate meme detection, presenting compelling findings.
no code implementations • 11 Feb 2023 • Piush Aggarwal, Pranit Chawla, Mithun Das, Punyajoy Saha, Binny Mathew, Torsten Zesch, Animesh Mukherjee
Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks.
no code implementations • 8 May 2021 • Aashish Agarwal, Torsten Zesch
When evaluating the performance of automatic speech recognition models, usually word error rate within a certain dataset is used.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
1 code implementation • KONVENS (WS) 2021 • Onno Eberhard, Torsten Zesch
In this paper, we investigate the effect of layer freezing on the effectiveness of model transfer in the area of automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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 • Venelin Kovatchev, Darina Gold, M. Antonia Marti, Maria Salamo, Torsten Zesch
We use the typology to annotate a corpus of 520 sentence pairs in English and we demonstrate that unlike previous typologies, SHARel can be applied to all relations of interest with a high inter-annotator agreement.
no code implementations • 7 Apr 2020 • Frederike Zufall, Marius Hamacher, Katharina Kloppenborg, Torsten Zesch
We propose a 'legal approach' to hate speech detection by operationalization of the decision as to whether a post is subject to criminal law into an NLP task.
no code implementations • RANLP 2019 • Darina Gold, Torsten Zesch
The resulting proposition evaluation dataset allows us to independently compare the performance of proposition extraction systems on simple and complex clauses.
1 code implementation • WS 2019 • Darina Gold, Venelin Kovatchev, Torsten Zesch
Here we present a corpus annotated with these relations and the analysis of these results.
1 code implementation • NAACL 2019 • Frederike Zufall, Tobias Horsmann, Torsten Zesch
In this article, we analyze which Twitter posts could actually be deemed offenses under German criminal law.
no code implementations • SEMEVAL 2019 • Huangpan Zhang, Michael Wojatzki, Tobias Horsmann, Torsten Zesch
On the Spanish data our system is ranked 25th out of 39.
no code implementations • SEMEVAL 2019 • Piush Aggarwal, Tobias Horsmann, Michael Wojatzki, Torsten Zesch
We present results for Subtask A and C of SemEval 2019 Shared Task 6.
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.
1 code implementation • SEMEVAL 2018 • Michael Wojatzki, Torsten Zesch, Saif Mohammad, Svetlana Kiritchenko
Being able to predict whether people agree or disagree with an assertion (i. e. an explicit, self-contained statement) has several applications ranging from predicting how many people will like or dislike a social media post to classifying posts based on whether they are in accordance with a particular point of view.
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 • 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 • 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 • EMNLP 2017 • Tobias Horsmann, Torsten Zesch
A recent study by Plank et al. (2016) found that LSTM-based PoS taggers considerably improve over the current state-of-the-art when evaluated on the corpora of the Universal Dependencies project that use a coarse-grained tagset.
1 code implementation • RANLP 2017 • Darina Benikova, Torsten Zesch
Paraphrases exist on different granularity levels, the most frequently used one being the sentential level.
no code implementations • COLING 2016 • Tobias Horsmann, Torsten Zesch
We propose a new approach to PoS tagging where in a first step, we assign a coarse-grained tag corresponding to the main syntactic category.
no code implementations • COLING 2016 • Ildik{\'o} Pil{\'a}n, Elena Volodina, Torsten Zesch
The lack of a sufficient amount of data tailored for a task is a well-recognized problem for many statistical NLP methods.
no code implementations • LREC 2016 • Torsten Zesch, Tobias Horsmann
We present FlexTag, a highly flexible PoS tagging framework.
no code implementations • TACL 2014 • Lisa Beinborn, Torsten Zesch, Iryna Gurevych
Language proficiency tests are used to evaluate and compare the progress of language learners.