67 citations · 361 across the 22 of their papers we have counts for
8 papers · 1 filter
Detecting Cross-Language Plagiarism using Open Knowledge Graphs
Johannes Stegmüller, Fabian Bauer-Marquart, Norman Meuschke +3
Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cros…
Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection
Jan Philip Wahle, Nischal Ashok, Terry Ruas +3
A drastic rise in potentially life-threatening misinformation has been a by-product of the COVID-19 pandemic. Computational support to identify false information within the massive…
Incorporating Word Sense Disambiguation in Neural Language Models
Jan Philip Wahle, Terry Ruas, Norman Meuschke +1
We present two supervised (pre-)training methods to incorporate gloss definitions from lexical resources into neural language models (LMs). The training improves our models' perfor…
Evaluating Document Representations for Content-based Legal Literature Recommendations
Malte Ostendorff, Elliott Ash, Terry Ruas +3
Recommender systems assist legal professionals in finding relevant literature for supporting their case. Despite its importance for the profession, legal applications do not reflec…
Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection
Jan Philip Wahle, Terry Ruas, Norman Meuschke +1
The rise of language models such as BERT allows for high-quality text paraphrasing. This is a problem to academic integrity, as it is difficult to differentiate between original an…
Identifying Machine-Paraphrased Plagiarism
Jan Philip Wahle, Terry Ruas, Tomáš Foltýnek +2
Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we evaluate the effectivenes…