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Found 19 papers, 8 papers with code
Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency
Collecting labeled datasets in finance is challenging due to scarcity of domain experts and higher cost of employing them.
Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling
We present an open-source, pip installable toolkit, Sig-Networks, the first of its kind for longitudinal language modelling.
PANACEA: An Automated Misinformation Detection System on COVID-19
In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection.
PHEMEPlus: Enriching Social Media Rumour Verification with External Evidence
Work on social media rumour verification utilises signals from posts, their propagation and users involved.
Building for Tomorrow: Assessing the Temporal Persistence of Text Classifiers
Therefore an ability to predict a model's ability to persist over time can help design models that can be effectively used over a longer period of time.
Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims
The dataset construction includes work on retrieval techniques and similarity measurements to ensure a unique set of claims.
Opinions are Made to be Changed: Temporally Adaptive Stance Classification
We propose a novel approach to mitigate this performance drop, which is based on temporal adaptation of the word embeddings used for training the stance classifier.
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies
Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature.
QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness Using an Enhanced CT-BERT with Numeric Expressions
The purpose of this task is to determine the check-worthiness of tweets about COVID-19 to identify and prioritise tweets that need fact-checking.
Estimating predictive uncertainty for rumour verification models
The inability to correctly resolve rumours circulating online can have harmful real-world consequences.
Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced Data
The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed.
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data
The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed.
SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour{'}s veracity.
RumourEval 2019: Determining Rumour Veracity and Support for Rumours
This is the proposal for RumourEval-2019, which will run in early 2019 as part of that year's SemEval event.
All-in-one: Multi-task Learning for Rumour Verification
We propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification.
Discourse-Aware Rumour Stance Classification in Social Media Using Sequential Classifiers
We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers.
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
This paper describes team Turing's submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A).
Ranked #1 on
Stance Detection
on RumourEval
Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest.
