no code implementations • 18 May 2024 • Anthony Hughes, Xingyi Song
We propose a novel system that can generate synthetic medical claims to aid each of these core tasks.
1 code implementation • 1 May 2024 • Yida Mu, Peizhen Bai, Kalina Bontcheva, Xingyi Song
In this paper, we focus on addressing the issues of topic granularity and hallucinations for better LLM-based topic modelling.
no code implementations • 24 Mar 2024 • Yida Mu, Chun Dong, Kalina Bontcheva, Xingyi Song
Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents.
no code implementations • 9 Nov 2023 • Ben Wu, Yue Li, Yida Mu, Carolina Scarton, Kalina Bontcheva, Xingyi Song
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks.
no code implementations • 20 Sep 2023 • Yida Mu, Xingyi Song, Kalina Bontcheva, Nikolaos Aletras
A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors.
no code implementations • 29 Aug 2023 • Tianyu Liang, Yida Mu, Soonho Kim, Darline Larissa Kengne Kuate, Julie Lang, Rob Vos, Xingyi Song
A large number of conflict events are affecting the world all the time.
1 code implementation • 14 Aug 2023 • Olesya Razuvayevskaya, Ben Wu, Joao A. Leite, Freddy Heppell, Ivan Srba, Carolina Scarton, Kalina Bontcheva, Xingyi Song
Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient.
1 code implementation • 12 Aug 2023 • Ambrose Robinson, William Thorne, Ben P. Wu, Abdullah Pandor, Munira Essat, Mark Stevenson, Xingyi Song
Medical systematic reviews can be very costly and resource intensive.
no code implementations • 10 Aug 2023 • Iknoor Singh, Carolina Scarton, Xingyi Song, Kalina Bontcheva
The task of retrieving already debunked narratives aims to detect stories that have already been fact-checked.
no code implementations • 23 May 2023 • Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song
Instruction-tuned Large Language Models (LLMs) have exhibited impressive language understanding and the capacity to generate responses that follow specific prompts.
no code implementations • 10 Apr 2023 • Yida Mu, Ye Jiang, Freddy Heppell, Iknoor Singh, Carolina Scarton, Kalina Bontcheva, Xingyi Song
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.
no code implementations • 10 Apr 2023 • Yida Mu, Mali Jin, Kalina Bontcheva, Xingyi Song
It is crucial for policymakers to have a comprehensive understanding of the public's stance towards vaccination on a large scale.
no code implementations • 9 Apr 2023 • Ye Jiang, Xiaomin Yu, Yimin Wang, Xiaoman Xu, Xingyi Song, Diana Maynard
First, we incorporate prompt learning into multimodal fake news detection.
1 code implementation • 16 Mar 2023 • Ben Wu, Olesya Razuvayevskaya, Freddy Heppell, João A. Leite, Carolina Scarton, Kalina Bontcheva, Xingyi Song
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.
1 code implementation • 17 Jan 2023 • Yida Mu, Mali Jin, Charlie Grimshaw, Carolina Scarton, Kalina Bontcheva, Xingyi Song
Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination.
no code implementations • 18 Jul 2022 • Yue Li, Carolina Scarton, Xingyi Song, Kalina Bontcheva
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.
no code implementations • 3 Sep 2021 • Ziqi Zhang, Xingyi Song
We process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating of language resources: training word embedding models, continued pre-training of BERT-like language models, and training Machine Translation models that are used as a proxy to generate product-related keywords.
no code implementations • 22 Jun 2021 • Ye Jiang, Xingyi Song, Carolina Scarton, Ahmet Aker, Kalina Bontcheva
In this paper, we introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation (e. g. comment or question to the misinformation).
no code implementations • 5 Jun 2020 • Xingyi Song, Johann Petrak, Ye Jiang, Iknoor Singh, Diana Maynard, Kalina Bontcheva
The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide.
no code implementations • LREC 2020 • Xingyi Song, Johnny Downs, Sumithra Velupillai, Rachel Holden, Maxim Kikoler, Kalina Bontcheva, Rina Dutta, Angus Roberts
Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk.
1 code implementation • LREC 2020 • Jie Gao, Sooji Han, Xingyi Song, Fabio Ciravegna
Early rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available.
no code implementations • SEMEVAL 2019 • Ye Jiang, Johann Petrak, Xingyi Song, Kalina Bontcheva, Diana Maynard
This paper describes the participation of team {``}bertha-von-suttner{''} in the SemEval2019 task 4 Hyperpartisan News Detection task.
1 code implementation • 12 Nov 2018 • Genevieve Gorrell, Xingyi Song, Angus Roberts
Ever-expanding volumes of biomedical text require automated semantic annotation techniques to curate and put to best use.
no code implementations • EMNLP 2018 • Xingyi Song, Johann Petrak, Angus Roberts
In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification.
no code implementations • WS 2017 • Ye Jiang, Xingyi Song, Jackie Harrison, Shaun Quegan, Diana Maynard
Preliminary analysis identifies clearly different attitudes on the same issue presented in different news sources.