527 citations · 1.9k across the 46 of their papers we have counts for
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LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
Ilias Chalkidis, Abhik Jana, Dirk Hartung +4
Laws and their interpretations, legal arguments and agreements\ are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which i…
EDGAR-CORPUS: Billions of Tokens Make The World Go Round
Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos +1
We release EDGAR-CORPUS, a novel corpus comprising annual reports from all the publicly traded companies in the US spanning a period of more than 25 years. To the best of our knowl…
MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer
Ilias Chalkidis, Manos Fergadiotis, Ion Androutsopoulos
We introduce MULTI-EURLEX, a new multilingual dataset for topic classification of legal documents. The dataset comprises 65k European Union (EU) laws, officially translated in 23 l…
A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections
Dimitris Pappas, Ion Androutsopoulos
Question answering (QA) systems for large document collections typically use pipelines that (i) retrieve possibly relevant documents, (ii) re-rank them, (iii) rank paragraphs or ot…
Deception detection in text and its relation to the cultural dimension of individualism/collectivism
Katerina Papantoniou, Panagiotis Papadakos, Theodore Patkos +3
Deception detection is a task with many applications both in direct physical and in computer-mediated communication. Our focus is on automatic deception detection in text across cu…
Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases
Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis +3
Interpretability or explainability is an emerging research field in NLP. From a user-centric point of view, the goal is to build models that provide proper justification for their…