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Found 77 papers, 26 papers with code
Revisiting Rumour Stance Classification: Dealing with Imbalanced Data
Correctly classifying stances of replies can be significantly helpful for the automatic detection and classification of online rumours.
Controlling Extra-Textual Attributes about Dialogue Participants: A Case Study of English-to-Polish Neural Machine Translation
We focus on the underresearched problem of utilising external metadata in automatic translation of TV dialogue, proposing a case study where a wide range of approaches for controlling attributes in translation is employed in a multi-attribute scenario.
The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification
Second, we conduct the first meta-evaluation of automatic metrics in Text Simplification, using our new data set (and other existing data) to analyze the variation of the correlation between metrics’ scores and human judgments across three dimensions: the perceived simplicity level, the system type, and the set of references used for computation.
GateNLP at SemEval-2025 Task 10: Hierarchical Three-Step Prompting for Multilingual Narrative Classification
The proliferation of online news and the increasing spread of misinformation necessitate robust methods for automatic data analysis.
SCRum-9: Multilingual Stance Classification over Rumours on Social Media
We introduce SCRum-9, a multilingual dataset for Rumour Stance Classification, containing 7, 516 tweet-reply pairs from X. SCRum-9 goes beyond existing stance classification datasets by covering more languages (9), linking examples to more fact-checked claims (2. 1k), and including complex annotations from multiple annotators to account for intra- and inter-annotator variability.
UKElectionNarratives: A Dataset of Misleading Narratives Surrounding Recent UK General Elections
Misleading narratives play a crucial role in shaping public opinion during elections, as they can influence how voters perceive candidates and political parties.
Exploring Vision Language Models for Multimodal and Multilingual Stance Detection
Our results show that VLMs generally rely more on text than images for stance detection and this trend persists across languages.
Leveraging Large Language Models for Zero-shot Lay Summarisation in Biomedicine and Beyond
In this work, we explore the application of Large Language Models to zero-shot Lay Summarisation.
A Cross-Domain Study of the Use of Persuasion Techniques in Online Disinformation
Disinformation, irrespective of domain or language, aims to deceive or manipulate public opinion, typically through employing advanced persuasion techniques.
Investigating Idiomaticity in Word Representations
Idiomatic expressions are an integral part of human languages, often used to express complex ideas in compressed or conventional ways (e. g. eager beaver as a keen and enthusiastic person).
A Survey on Automatic Credibility Assessment of Textual Credibility Signals in the Era of Large Language Models
In the current era of social media and generative AI, an ability to automatically assess the credibility of online social media content is of tremendous importance.
Label Set Optimization via Activation Distribution Kurtosis for Zero-shot Classification with Generative Models
In-context learning (ICL) performance is known to be sensitive to the prompt design, yet the impact of class label options in zero-shot classification has been largely overlooked.
Overview of the BioLaySumm 2024 Shared Task on the Lay Summarization of Biomedical Research Articles
This paper presents the setup and results of the second edition of the BioLaySumm shared task on the Lay Summarisation of Biomedical Research Articles, hosted at the BioNLP Workshop at ACL 2024.
A Case Study on Contextual Machine Translation in a Professional Scenario of Subtitling
Incorporating extra-textual context such as film metadata into the machine translation (MT) pipeline can enhance translation quality, as indicated by automatic evaluation in recent work.
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss
Accurately modeling idiomatic or non-compositional language has been a longstanding challenge in Natural Language Processing (NLP).
EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles
This work introduces EUvsDisinfo, a multilingual dataset of disinformation articles originating from pro-Kremlin outlets, along with trustworthy articles from credible / less biased sources.
ATLAS: Improving Lay Summarisation with Attribute-based Control
Lay summarisation aims to produce summaries of scientific articles that are comprehensible to non-expert audiences.
ExU: AI Models for Examining Multilingual Disinformation Narratives and Understanding their Spread
We describe the ExU project proposal and summarise the results of a user requirements survey regarding the design of tools to support fact-checking.
Word Boundary Information Isn't Useful for Encoder Language Models
As such, removing these symbols has been shown to have a beneficial effect on the processing of morphologically complex words for transformer encoders in the pretrain-finetune paradigm.
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks.
Enhancing Biomedical Lay Summarisation with External Knowledge Graphs
Previous approaches for automatic lay summarisation are exclusively reliant on the source article that, given it is written for a technical audience (e. g., researchers), is unlikely to explicitly define all technical concepts or state all of the background information that is relevant for a lay audience.
Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study
This paper analyses two hitherto unstudied sites sharing state-backed disinformation, Reliable Recent News (rrn. world) and WarOnFakes (waronfakes. com), which publish content in Arabic, Chinese, English, French, German, and Spanish.
Overview of the BioLaySumm 2023 Shared Task on Lay Summarization of Biomedical Research Articles
This paper presents the results of the shared task on Lay Summarisation of Biomedical Research Articles (BioLaySumm), hosted at the BioNLP Workshop at ACL 2023.
Weakly Supervised Veracity Classification with LLM-Predicted Credibility Signals
This paper introduces Pastel (Prompted weAk Supervision wiTh crEdibility signaLs), a weakly supervised approach that leverages large language models (LLMs) to extract credibility signals from web content, and subsequently combines them to predict the veracity of content without relying on human supervision.
Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification
Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient.
Multilingual text classification
parameter-efficient fine-tuning
+2
Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checking
Finding previously debunked narratives involves identifying claims that have already undergone fact-checking.
Noisy Self-Training with Data Augmentations for Offensive and Hate Speech Detection Tasks
Online social media is rife with offensive and hateful comments, prompting the need for their automatic detection given the sheer amount of posts created every second.
Ranked #1 on
Hate Speech Detection
on OLID
(using extra training data)
MTCue: Learning Zero-Shot Control of Extra-Textual Attributes by Leveraging Unstructured Context in Neural Machine Translation
This work introduces MTCue, a novel neural machine translation (NMT) framework that interprets all context (including discrete variables) as text.
Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science
Instruction-tuned Large Language Models (LLMs) have exhibited impressive language understanding and the capacity to generate responses that follow specific prompts.
A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation
This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets.
Reference-less Analysis of Context Specificity in Translation with Personalised Language Models
Our results suggest that the degree to which professional translations in our domain are context-specific can be preserved to a better extent by a contextual machine translation model than a non-contextual model, which is also reflected in the contextual model's superior reference-based scores.
Can We Identify Stance Without Target Arguments? A Study for Rumour Stance Classification
Considering a conversation thread, rumour stance classification aims to identify the opinion (e. g. agree or disagree) of replies towards a target (rumour story).
SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification
For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensembles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining.
VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter
Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination.
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
Lay summarisation aims to jointly summarise and simplify a given text, thus making its content more comprehensible to non-experts.
Ranked #1 on
Lay Summarization
on PLOS
Classifying COVID-19 vaccine narratives
This paper addresses the need for monitoring and analysing vaccine narratives online by introducing a novel vaccine narrative classification task, which categorises COVID-19 vaccine claims into one of seven categories.
GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity
This paper describes the second-placed system on the leaderboard of SemEval-2022 Task 8: Multilingual News Article Similarity.
Sample Efficient Approaches for Idiomaticity Detection
In particular we study the impact of Pattern Exploit Training (PET), a few-shot method of classification, and BERTRAM, an efficient method of creating contextual embeddings, on the task of idiomaticity detection.
Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022
This paper describes the SLT-CDT-UoS group's submission to the first Special Task on Formality Control for Spoken Language Translation, part of the IWSLT 2022 Evaluation Campaign.
Controlling Extra-Textual Attributes about Dialogue Participants -- A Case Study of English-to-Polish Neural Machine Translation
We focus on the underresearched problem of utilising external metadata in automatic translation of TV dialogue, proposing a case study where a wide range of approaches for controlling attributes in translation is employed in a multi-attribute scenario.
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding
This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two subtasks: (a) a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and (b) a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context.
Improving Tokenisation by Alternative Treatment of Spaces
We find that our modified algorithms lead to improved performance on downstream NLP tasks that involve handling complex words, whilst having no detrimental effect on performance in general natural language understanding tasks.
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models
Despite their success in a variety of NLP tasks, pre-trained language models, due to their heavy reliance on compositionality, fail in effectively capturing the meanings of multiword expressions (MWEs), especially idioms.
Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels
This paper presents the Noun Compound Type and Token Idiomaticity (NCTTI) dataset, with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token level.
Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic
In this paper, we introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation (e. g. comment or question to the misinformation).
Probing for idiomaticity in vector space models
Contextualised word representation models have been successfully used for capturing different word usages and they may be an attractive alternative for representing idiomaticity in language.
Multistage BiCross encoder for multilingual access to COVID-19 health information
The Coronavirus (COVID-19) pandemic has led to a rapidly growing 'infodemic' of health information online.
Measuring What Counts: The case of Rumour Stance Classification
This paper specifically questions the evaluation metrics used in these shared tasks.
Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis
Therefore, identifying these comments is an important task for studying and preventing the proliferation of toxicity in social media.
Ranked #1 on
Hate Speech Detection
on ToLD-Br
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
Furthermore, we motivate the need for developing better methods for automatic evaluation using ASSET, since we show that current popular metrics may not be suitable when multiple simplification transformations are performed.
Measuring the Impact of Readability Features in Fake News Detection
The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health.
Data-Driven Sentence Simplification: Survey and Benchmark
Sentence Simplification (SS) aims to modify a sentence in order to make it easier to read and understand.
Estimating post-editing effort: a study on human judgements, task-based and reference-based metrics of MT quality
To that end, we report experiments on a dataset with newly-collected post-editing indicators and show their usefulness when estimating post-editing effort.
EASSE: Easier Automatic Sentence Simplification Evaluation
We introduce EASSE, a Python package aiming to facilitate and standardise automatic evaluation and comparison of Sentence Simplification (SS) systems.
Cross-Sentence Transformations in Text Simplification
Current approaches to Text Simplification focus on simplifying sentences individually.
Sheffield Submissions for WMT18 Multimodal Translation Shared Task
For task 1b, we explore three approaches: (i) re-ranking based on cross-lingual word sense disambiguation (as for task 1), (ii) re-ranking based on consensus of NMT n-best lists from German-Czech, French-Czech and English-Czech systems, and (iii) data augmentation by generating English source data through machine translation from French to English and from German to English followed by hypothesis selection using a multimodal-reranker.
Sheffield Submissions for the WMT18 Quality Estimation Shared Task
In this paper we present the University of Sheffield submissions for the WMT18 Quality Estimation shared task.
Exploring Gap Filling as a Cheaper Alternative to Reading Comprehension Questionnaires when Evaluating Machine Translation for Gisting
A popular application of machine translation (MT) is gisting: MT is consumed as is to make sense of text in a foreign language.
Learning Simplifications for Specific Target Audiences
Text simplification (TS) is a monolingual text-to-text transformation task where an original (complex) text is transformed into a target (simpler) text.
MUSST: A Multilingual Syntactic Simplification Tool
Our implementation includes a set of general-purpose simplification rules, as well as a sentence selection module (to select sentences to be simplified) and a confidence model (to select only promising simplifications).
Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs
Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data.
Ranked #8 on
Text Simplification
on PWKP / WikiSmall
(SARI metric)
Improving Evaluation of Document-level Machine Translation Quality Estimation
Meaningful conclusions about the relative performance of NLP systems are only possible if the gold standard employed in a given evaluation is both valid and reliable.
Quality Estimation for Language Output Applications
The goal of QE is to estimate the quality of language output applications without the need of human references.
A Reading Comprehension Corpus for Machine Translation Evaluation
Effectively assessing Natural Language Processing output tasks is a challenge for research in the area.
