Search Results for author: Viktor Schlegel

Found 18 papers, 8 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

A Two-Stage Decoder for Efficient ICD Coding

1 code implementation27 May 2023 Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Kashyap, Stefan Winkler

Clinical notes in healthcare facilities are tagged with the International Classification of Diseases (ICD) code; a list of classification codes for medical diagnoses and procedures.

Multilabel Text Classification text-classification +1

Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation

no code implementations25 May 2023 Hao Li, Viktor Schlegel, Riza Batista-Navarro, Goran Nenadic

Furthermore, evaluating key points is crucial in ensuring that the automatically generated summaries are useful.

Beyond Words: A Comprehensive Survey of Sentence Representations

no code implementations22 May 2023 Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Viktor Schlegel, Stefan Winkler, See-Kiong Ng, Soujanya Poria

In this paper, we provide an overview of the different methods for sentence representation learning, including both traditional and deep learning-based techniques.

Question Answering Representation Learning +4

Towards Human-Centred Explainability Benchmarks For Text Classification

no code implementations10 Nov 2022 Viktor Schlegel, Erick Mendez-Guzman, Riza Batista-Navarro

Progress on many Natural Language Processing (NLP) tasks, such as text classification, is driven by objective, reproducible and scalable evaluation via publicly available benchmarks.

Misinformation Sentiment Analysis +3

Can Transformers Reason in Fragments of Natural Language?

1 code implementation10 Nov 2022 Viktor Schlegel, Kamen V. Pavlov, Ian Pratt-Hartmann

State-of-the-art deep-learning-based approaches to Natural Language Processing (NLP) are credited with various capabilities that involve reasoning with natural language texts.


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.


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

Cannot find the paper you are looking for? You can Submit a new open access paper.