SocialPET: Socially Informed Pattern Exploiting Training for Few-Shot Stance Detection in Social Media

Stance detection, as the task of determining the viewpoint of a social media post towards a target as 'favor' or 'against', has been understudied in the challenging yet realistic scenario where there is limited labeled data for a certain target. Our work advances research in few-shot stance detection by introducing SocialPET, a socially informed approach to leveraging language models for the task. Our proposed approach builds on the Pattern Exploiting Training (PET) technique, which addresses classification tasks as cloze questions through the use of language models. To enhance the approach with social awareness, we exploit the social network structure surrounding social media posts. We prove the effectiveness of SocialPET on two stance datasets, Multi-target and P-Stance, outperforming competitive stance detection models as well as the base model, PET, where the labeled instances for the target under study is as few as 100. When we delve into the results, we observe that SocialPET is comparatively strong in identifying instances of the `against' class, where baseline models underperform.

ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection

Swear words are a common proxy to collect datasets with cyberbullying incidents. Our focus is on measuring and mitigating biases derived from spurious associations between swear words and incidents occurring as a result of such data collection strategies. After demonstrating and quantifying these biases, we introduce ID-XCB, the first data-independent debiasing technique that combines adversarial training, bias constraints and debias fine-tuning approach aimed at alleviating model attention to bias-inducing words without impacting overall model performance. We explore ID-XCB on two popular session-based cyberbullying datasets along with comprehensive ablation and generalisation studies. We show that ID-XCB learns robust cyberbullying detection capabilities while mitigating biases, outperforming state-of-the-art debiasing methods in both performance and bias mitigation. Our quantitative and qualitative analyses demonstrate its generalisability to unseen data.

D-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection

Swear words are a common proxy to collect datasets with cyberbullying incidents. Our focus is on measuring and mitigating biases derived from spurious associations between swear words and incidents occurring as a result of such data collection strategies. After demonstrating and quantifying these biases, we introduce ID-XCB, the first data-independent debiasing technique that combines adversarial training, bias constraints and debias fine-tuning approach aimed at alleviating model attention to bias-inducing words without impacting overall model performance. We explore ID-XCB on two popular session-based cyberbullying datasets along with comprehensive ablation and generalisation studies. We show that ID-XCB learns robust cyberbullying detection capabilities while mitigating biases, outperforming state-of-the-art debiasing methods in both performance and bias mitigation. Our quantitative and qualitative analyses demonstrate its generalisability to unseen data.

MAPLE: Micro Analysis of Pairwise Language Evolution for Few-Shot Claim Verification

Claim verification is an essential step in the automated fact-checking pipeline which assesses the veracity of a claim against a piece of evidence. In this work, we explore the potential of few-shot claim verification, where only very limited data is available for supervision. We propose MAPLE (Micro Analysis of Pairwise Language Evolution), a pioneering approach that explores the alignment between a claim and its evidence with a small seq2seq model and a novel semantic measure. Its innovative utilization of micro language evolution path leverages unlabelled pairwise data to facilitate claim verification while imposing low demand on data annotations and computing resources. MAPLE demonstrates significant performance improvements over SOTA baselines SEED, PET and LLaMA 2 across three fact-checking datasets: FEVER, Climate FEVER, and SciFact. Data and code are available here: https://github.com/XiaZeng0223/MAPLE

Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research

Automated fact-checking has drawn considerable attention over the past few decades due to the increase in the diffusion of misinformation on online platforms. This is often carried out as a sequence of tasks comprising (i) the detection of sentences circulating in online platforms which constitute claims needing verification, followed by (ii) the verification process of those claims. This survey focuses on the former, by discussing existing efforts towards detecting claims needing fact-checking, with a particular focus on multilingual data and methods. This is a challenging and fertile direction where existing methods are yet far from matching human performance due to the profoundly challenging nature of the issue. Especially, the dissemination of information across multiple social platforms, articulated in multiple languages and modalities demands more generalized solutions for combating misinformation. Focusing on multilingual misinformation, we present a comprehensive survey of existing multilingual claim detection research. We present state-of-the-art multilingual claim detection research categorized into three key factors of the problem, verifiability, priority, and similarity. Further, we present a detailed overview of the existing multilingual datasets along with the challenges and suggest possible future advancements.

Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid Approaches to Natural Language Processing

The advancement of machine learning and symbolic approaches have underscored their strengths and weaknesses in Natural Language Processing (NLP). While machine learning approaches are powerful in identifying patterns in data, they often fall short in learning commonsense and the factual knowledge required for the NLP tasks. Meanwhile, the symbolic methods excel in representing knowledge-rich data. However, they struggle to adapt dynamic data and generalize the knowledge. Bridging these two paradigms through hybrid approaches enables the alleviation of weaknesses in both while preserving their strengths. Recent studies extol the virtues of this union, showcasing promising results in a wide range of NLP tasks. In this paper, we present an overview of hybrid approaches used for NLP. Specifically, we delve into the state-of-the-art hybrid approaches used for a broad spectrum of NLP tasks requiring natural language understanding, generation, and reasoning. Furthermore, we discuss the existing resources available for hybrid approaches for NLP along with the challenges, offering a roadmap for future directions.

Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges

The growing prevalence and rapid evolution of offensive language in social media amplify the complexities of detection, particularly highlighting the challenges in identifying such content across diverse languages. This survey presents a systematic and comprehensive exploration of Cross-Lingual Transfer Learning (CLTL) techniques in offensive language detection in social media. Our study stands as the first holistic overview to focus exclusively on the cross-lingual scenario in this domain. We analyse 67 relevant papers and categorise these studies across various dimensions, including the characteristics of multilingual datasets used, the cross-lingual resources employed, and the specific CLTL strategies implemented. According to "what to transfer", we also summarise three main CLTL transfer approaches: instance, feature, and parameter transfer. Additionally, we shed light on the current challenges and future research opportunities in this field. Furthermore, we have made our survey resources available online, including two comprehensive tables that provide accessible references to the multilingual datasets and CLTL methods used in the reviewed literature.

Faithful Knowledge Graph Explanations for Commonsense Reasoning

While fusing language models (LMs) and knowledge graphs (KGs) has become common in commonsense question answering research, enabling faithful chain-of-thought explanations in these models remains an open problem. One major weakness of current KG-based explanation techniques is that they overlook the faithfulness of generated explanations during evaluation. To address this gap, we make two main contributions: (1) We propose and validate two quantitative metrics - graph consistency and graph fidelity - to measure the faithfulness of KG-based explanations. (2) We introduce Consistent GNN (CGNN), a novel training method that adds a consistency regularization term to improve explanation faithfulness. Our analysis shows that predictions from KG often diverge from original model predictions. The proposed CGNN approach boosts consistency and fidelity, demonstrating its potential for producing more faithful explanations. Our work emphasises the importance of explicitly evaluating suggest a path forward for developing architectures for faithful graph-based explanations.

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. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available.

Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes

Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes. Research enabling automated analysis of memes has gained attention in recent years, including among others the task of classifying the emotion expressed in memes. In this paper, we propose a novel model, cluster-based deep ensemble learning (CDEL), for emotion classification in memes. CDEL is a hybrid model that leverages the benefits of a deep learning model in combination with a clustering algorithm, which enhances the model with additional information after clustering memes with similar facial features. We evaluate the performance of CDEL on a benchmark dataset for emotion classification, proving its effectiveness by outperforming a wide range of baseline models and achieving state-of-the-art performance. Further evaluation through ablated models demonstrates the effectiveness of the different components of CDEL.