Search Results for author: Viktor Schlegel

Found 10 papers, 3 papers with code

Incorporating Zoning Information into Argument Mining from Biomedical Literature

no code implementations LREC 2022 Boyang Liu, Viktor Schlegel, Riza Batista-Navarro, Sophia Ananiadou

Argumentative zoning, a specific text zoning scheme for the scientific domain, is considered as the antecedent for argument mining by many researchers.

Argument Mining

‘Am I the Bad One’? Predicting the Moral Judgement of the Crowd Using Pre–trained Language Models

no code implementations LREC 2022 Areej Alhassan, Jinkai Zhang, Viktor Schlegel

This paper studies whether state-of-the-art, pre-trained language models are capable of passing moral judgments on posts retrieved from a popular Reddit user board.

Anomaly Detection Natural Language Inference

RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour

no code implementations LREC 2022 Erick Mendez Guzman, Viktor Schlegel, Riza Batista-Navarro

Each news article was annotated for two aspects: (1) indicators of forced labour as classification labels and (2) snippets of the text that justify labelling decisions.

Multi Label Text Classification Multi-Label Text Classification +1

WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language

no code implementations ACL 2022 Federico Tavella, Viktor Schlegel, Marta Romeo, Aphrodite Galata, Angelo Cangelosi

Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals.

Translation

Semantics Altering Modifications for Evaluating Comprehension in Machine Reading

1 code implementation7 Dec 2020 Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro

Advances in NLP have yielded impressive results for the task of machine reading comprehension (MRC), with approaches having been reported to achieve performance comparable to that of humans.

Machine Reading Comprehension

Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models

no code implementations29 May 2020 Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro

Recent years have seen a growing number of publications that analyse Natural Language Inference (NLI) datasets for superficial cues, whether they undermine the complexity of the tasks underlying those datasets and how they impact those models that are optimised and evaluated on this data.

Natural Language Inference

Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks

no code implementations WS 2019 Mokanarangan Thayaparan, Marco Valentino, Viktor Schlegel, Andre Freitas

Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text.

Multi-hop Question Answering Question Answering +1

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