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Found 261 papers, 97 papers with code
COVID-19 in Bulgarian Social Media: Factuality, Harmfulness, Propaganda, and Framing
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic.
Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages
Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task.
Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes
We present the findings of the shared task at the CONSTRAINT 2022 Workshop: Hero, Villain, and Victim: Dissecting harmful memes for Semantic role labeling of entities.
MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation
Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation.
Arabic Dataset for LLM Safeguard Evaluation
The growing use of large language models (LLMs) has raised concerns regarding their safety.
DocEdit-v2: Document Structure Editing Via Multimodal LLM Grounding
Document structure editing involves manipulating localized textual, visual, and layout components in document images based on the user's requests.
FIRE: Fact-checking with Iterative Retrieval and Verification
The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by a verification step.
Loki: An Open-Source Tool for Fact Verification
We introduce Loki, an open-source tool designed to address the growing problem of misinformation.
Exploring Language Model Generalization in Low-Resource Extractive QA
In this paper, we investigate Extractive Question Answering (EQA) with Large Language Models (LLMs) under domain drift, i. e., can LLMs generalize well to closed-domains that require specific knowledge such as medicine and law in a zero-shot fashion without additional in-domain training?
Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect
We introduce Atlas-Chat, the first-ever collection of LLMs specifically developed for dialectal Arabic.
PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent
Propaganda plays a critical role in shaping public opinion and fueling disinformation.
TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning
Current Large Language Models (LLMs) exhibit limited ability to understand table structures and to apply precise numerical reasoning, which is crucial for tasks such as table question answering (TQA) and table-based fact verification (TFV).
Post-OCR Text Correction for Bulgarian Historical Documents
We further develop a method for automatically generating synthetic data in this orthography, as well as in the subsequent Ivanchev orthography, by leveraging vast amounts of contemporary literature Bulgarian texts.
Optical Character Recognition
Optical Character Recognition (OCR)
Grounding Fallacies Misrepresenting Scientific Publications in Evidence
Health-related misinformation claims often falsely cite a credible biomedical publication as evidence, which superficially appears to support the false claim.
Unconditional Truthfulness: Learning Conditional Dependency for Uncertainty Quantification of Large Language Models
Uncertainty quantification (UQ) is a perspective approach to detecting Large Language Model (LLM) hallucinations and low quality output.
LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education.
OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs
The increased use of large language models (LLMs) across a variety of real-world applications calls for automatic tools to check the factual accuracy of their outputs, as LLMs often hallucinate.
Bilingual Adaptation of Monolingual Foundation Models
By continually pre-training on a mix of Arabic and English corpora, the model retains its proficiency in English while acquiring capabilities in Arabic.
Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs
To address this problem, we propose $\texttt{Web2Code}$, a benchmark consisting of a new large-scale webpage-to-code dataset for instruction tuning and an evaluation framework for the webpage understanding and HTML code translation abilities of MLLMs.
Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
However, research to date on UQ methods for LLMs has been fragmented, in terms of the literature on UQ techniques and evaluation methods.
Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs
Our systematic exploration of LMs' understanding of empathy reveals substantial opportunities for further investigation in both task formulation and modeling.
Exploring the Limitations of Detecting Machine-Generated Text
We find that classifiers are highly sensitive to stylistic changes and differences in text complexity, and in some cases degrade entirely to random classifiers.
Corpus Poisoning via Approximate Greedy Gradient Descent
Dense retrievers are widely used in information retrieval and have also been successfully extended to other knowledge intensive areas such as language models, e. g., Retrieval-Augmented Generation (RAG) systems.
Missci: Reconstructing Fallacies in Misrepresented Science
Unlike previous fallacy detection datasets, Missci (i) focuses on implicit fallacies between the relevant content of the cited publication and the inaccurate claim, and (ii) requires models to verbalize the fallacious reasoning in addition to classifying it.
MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing
Memes have evolved as a prevalent medium for diverse communication, ranging from humour to propaganda.
OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
Can a Multichoice Dataset be Repurposed for Extractive Question Answering?
The rapid evolution of Natural Language Processing (NLP) has favored major languages such as English, leaving a significant gap for many others due to limited resources.
SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection
The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30).
EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models
Solving the problems in the dataset requires advanced perception and joint reasoning over the text and the visual content of the image.
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output.
Multimodal Large Language Models to Support Real-World Fact-Checking
To the best of our knowledge, we are the first to evaluate MLLMs for real-world fact-checking.
ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic
The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models.
A Chinese Dataset for Evaluating the Safeguards in Large Language Models
Previous studies have proposed comprehensive taxonomies of the risks posed by LLMs, as well as corresponding prompts that can be used to examine the safety mechanisms of LLMs.
M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels.
Factuality of Large Language Models: A Survey
Large language models (LLMs), especially when instruction-tuned for chat, have become part of our daily lives, freeing people from the process of searching, extracting, and integrating information from multiple sources by offering a straightforward answer to a variety of questions in a single place.
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models
In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content.
Generating Zero-shot Abstractive Explanations for Rumour Verification
The task of rumour verification in social media concerns assessing the veracity of a claim on the basis of conversation threads that result from it.
LLM360: Towards Fully Transparent Open-Source LLMs
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition
In this study, we aim to enhance existing models for fallacy recognition by incorporating additional context and by leveraging large language models to generate synthetic data, thus increasing the representation of the infrequent classes.
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
A Survey of Confidence Estimation and Calibration in Large Language Models
Assessing their confidence and calibrating them across different tasks can help mitigate risks and enable LLMs to produce better generations.
A Template Is All You Meme
Here, to aid understanding of memes, we release a knowledge base of memes and information found on www. knowyourmeme. com, which we call the Know Your Meme Knowledge Base (KYMKB), composed of more than 54, 000 images.
ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text
We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP 2023 conference co-located with EMNLP 2023.
Adapting Fake News Detection to the Era of Large Language Models
With the proliferation of both human-written and machine-generated real and fake news, robustly and effectively discerning the veracity of news articles has become an intricate challenge.
Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media
Claim span identification (CSI) is an important step in fact-checking pipelines, aiming to identify text segments that contain a checkworthy claim or assertion in a social media post.
Quality > Quantity: Synthetic Corpora from Foundation Models for Closed-Domain Extractive Question Answering
Domain adaptation, the process of training a model in one domain and applying it to another, has been extensively explored in machine learning.
From Chaos to Clarity: Claim Normalization to Empower Fact-Checking
To evaluate the effectiveness of our proposed model, we meticulously compile a comprehensive real-world dataset, CLAN, comprising more than 6k instances of social media posts alongside their respective normalized claims.
QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking
Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them.
Factuality Challenges in the Era of Large Language Models
The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention.
Rethinking STS and NLI in Large Language Models
Recent years have seen the rise of large language models (LLMs), where practitioners use task-specific prompts; this was shown to be effective for a variety of tasks.
Fake News Detectors are Biased against Texts Generated by Large Language Models
The spread of fake news has emerged as a critical challenge, undermining trust and posing threats to society.
Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles
Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information.
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models
We release two open versions of the model -- the foundation Jais model, and an instruction-tuned Jais-chat variant -- with the aim of promoting research on Arabic LLMs.
Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs
With the rapid evolution of large language models (LLMs), new and hard-to-predict harmful capabilities are emerging.
bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark
We run the first systematic evaluation of pre-trained language models for Bulgarian, comparing and contrasting results across the nine tasks in the benchmark.
Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data
The need for more usable and explainable machine learning models in healthcare increases the importance of developing and utilizing causal discovery algorithms, which aim to discover causal relations by analyzing observational data.
Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data
Developing tools to automatically detect check-worthy claims in political debates and speeches can greatly help moderators of debates, journalists, and fact-checkers.
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
These results show that the problem is far from solved and that there is a lot of room for improvement.
DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text
One is called DetectLLM-LRR, which is fast and efficient, and the other is called DetectLLM-NPR, which is more accurate, but slower due to the need for perturbations.
On the Risk of Misinformation Pollution with Large Language Models
In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact on information-intensive applications, particularly Open-Domain Question Answering (ODQA) systems.
Detecting Propaganda Techniques in Code-Switched Social Media Text
Yet, it is common to find a mix of multiple languages in social media communication, a phenomenon known as code-switching.
Fact-Checking Complex Claims with Program-Guided Reasoning
Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning.
SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables
Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence.
QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models
This paper describes our participating system to this task.
MarsEclipse at SemEval-2023 Task 3: Multi-Lingual and Multi-Label Framing Detection with Contrastive Learning
This paper describes our system for SemEval-2023 Task 3 Subtask 2 on Framing Detection.
Automated Mapping of CVE Vulnerability Records to MITRE CWE Weaknesses
In recent years, a proliferation of cyber-security threats and diversity has been on the rise culminating in an increase in their reporting and analysis.
Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textual, visual and audio features for diverse cross-modal and modern multimodal tasks.
Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?
A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities.
Temporal Dynamics of Coordinated Online Behavior: Stability, Archetypes, and Influence
Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior.
What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes
Here, we introduce a novel task - EXCLAIM, generating explanations for visual semantic role labeling in memes.
Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic
Thus, there has been a lot of recent research on automatic detection of propaganda techniques in text as well as in memes.
GREENER: Graph Neural Networks for News Media Profiling
We study the problem of profiling news media on the Web with respect to their factuality of reporting and bias.
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training
In particular, on the complex set of TabFact, which contains multiple operations, PASTA largely outperforms the previous state of the art by 4. 7 points (85. 6% vs. 80. 9%), and the gap between PASTA and human performance on the small TabFact test set is narrowed to just 1. 5 points (90. 6% vs. 92. 1%).
Ranked #2 on
Table-based Fact Verification
on TabFact
IITD at the WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection
In addition to finding the techniques, Subtask 2 further asks to identify the textual span for each instance of each technique that is present in the tweet; the task can be modeled as a sequence tagging problem.
CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media
Thus, an interesting approach has emerged: to perform automatic fact-checking by verifying whether an input claim has been previously fact-checked by professional fact-checkers and to return back an article that explains their decision.
Ten Years after ImageNet: A 360° Perspective on AI
The rise of attention networks, self-supervised learning, generative modeling, and graph neural networks has widened the application space of AI.
DISARM: Detecting the Victims Targeted by Harmful Memes
Finally, we show that DISARM is interpretable and comparatively more generalizable and that it can reduce the relative error rate for harmful target identification by up to 9 points absolute over several strong multimodal rivals.
TeamX@DravidianLangTech-ACL2022: A Comparative Analysis for Troll-Based Meme Classification
The spread of fake news, propaganda, misinformation, disinformation, and harmful content online raised concerns among social media platforms, government agencies, policymakers, and society as a whole.
Detecting the Role of an Entity in Harmful Memes: Techniques and Their Limitations
The content that is posted and shared online can be textual, visual, or a combination of both, e. g., in a meme.
Detecting and Understanding Harmful Memes: A Survey
One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.
Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation
Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation.
QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Media
Fighting the ongoing COVID-19 infodemic has been declared as one of the most important focus areas by the World Health Organization since the onset of the COVID-19 pandemic.
Leaf: Multiple-Choice Question Generation
Testing with quiz questions has proven to be an effective way to assess and improve the educational process.
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge
In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain.
Batch-Softmax Contrastive Loss for Pairwise Sentence Scoring Tasks
The use of contrastive loss for representation learning has become prominent in computer vision, and it is now getting attention in Natural Language Processing (NLP).
Translating from Morphologically Complex Languages: A Paraphrase-Based Approach
We propose a novel approach to translating from a morphologically complex language.
Analyzing the Use of Character-Level Translation with Sparse and Noisy Datasets
This paper provides an analysis of character-level machine translation models used in pivot-based translation when applied to sparse and noisy datasets, such as crowdsourced movie subtitles.
Sentiment Analysis in Twitter for Macedonian
We present work on sentiment analysis in Twitter for Macedonian.
The Spread of Propaganda by Coordinated Communities on Social Media
Large-scale manipulations on social media have two important characteristics: (i) use of propaganda to influence others, and (ii) adoption of coordinated behavior to spread it and to amplify its impact.
Exposing Paid Opinion Manipulation Trolls
Recently, Web forums have been invaded by opinion manipulation trolls.
Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields
The paper presents a feature-rich approach to the automatic recognition and categorization of named entities (persons, organizations, locations, and miscellaneous) in news text for Bulgarian.
SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering
We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering.
Improved statistical machine translation using monolingual paraphrases
We propose a novel monolingual sentence paraphrasing method for augmenting the training data for statistical machine translation systems "for free" -- by creating it from data that is already available rather than having to create more aligned data.
Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims
We present an overview of the second edition of the CheckThat!
Detecting Harmful Memes and Their Targets
In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target.
RuleBert: Teaching Soft Rules to Pre-trained Language Models
While pre-trained language models (PLMs) are the go-to solution to tackle many natural language processing problems, they are still very limited in their ability to capture and to use common-sense knowledge.
Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News
We describe the fourth edition of the CheckThat!
Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection
Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used.
A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar
While COVID-19 vaccines are finally becoming widely available, a second pandemic that revolves around the circulation of anti-vaxxer fake news may hinder efforts to recover from the first one.
Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document
The output is a re-ranked list of the document sentences, so that those that can be verified are ranked as high as possible, together with corresponding evidence.
Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training
Most research in stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection.
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets
We focus on two tasks: (i)detecting harmful memes, and (ii)identifying the social entities they target.
Interpretable Propaganda Detection in News Articles
Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis.
Generating Answer Candidates for Quizzes and Answer-Aware Question Generators
In education, open-ended quiz questions have become an important tool for assessing the knowledge of students.
Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels
We propose a novel framework for predicting the factuality of reporting of news media outlets by studying the user attention cycles in their YouTube channels.
Detecting Propaganda Techniques in Memes
We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both.
AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking
With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that support multiple languages.
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images
We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems.
WhatTheWikiFact: Fact-Checking Claims Against Wikipedia
The rise of Internet has made it a major source of information.
Cross-Domain Label-Adaptive Stance Detection
In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them.
The Role of Context in Detecting Previously Fact-Checked Claims
Recent years have seen the proliferation of disinformation and fake news online.
Transformers: "The End of History" for NLP?
Recent advances in neural architectures, such as the Transformer, coupled with the emergence of large-scale pre-trained models such as BERT, have revolutionized the field of Natural Language Processing (NLP), pushing the state of the art for a number of NLP tasks.
A Neighbourhood Framework for Resource-Lean Content Flagging
The framework is based on a nearest-neighbour architecture.
A Survey on Predicting the Factuality and the Bias of News Media
The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim or article, either manually or automatically.
A Survey on Multimodal Disinformation Detection
As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content.
Automated Fact-Checking for Assisting Human Fact-Checkers
The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism.
Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go
The proliferation of harmful content on online platforms is a major societal problem, which comes in many different forms including hate speech, offensive language, bullying and harassment, misinformation, spam, violence, graphic content, sexual abuse, self harm, and many other.
A Survey on Stance Detection for Mis- and Disinformation Identification
Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information).
SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles
We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles.
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks
Attention mechanisms play a crucial role in the neural revolution of Natural Language Processing (NLP).
EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering
We perform various experiments with existing top-performing multilingual pre-trained models and we show that EXAMS offers multiple challenges that require multilingual knowledge and reasoning in multiple domains.
Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era
The rise of social media has democratized content creation and has made it easy for everybody to share and spread information online.
We Can Detect Your Bias: Predicting the Political Ideology of News Articles
We explore the task of predicting the leading political ideology or bias of news articles.
Vector-Vector-Matrix Architecture: A Novel Hardware-Aware Framework for Low-Latency Inference in NLP Applications
To address this issue, we propose a novel vector-vector-matrix architecture (VVMA), which greatly reduces the latency at inference time for NMT.
Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models
While misinformation and disinformation have been thriving in social media for years, with the emergence of the COVID-19 pandemic, the political and the health misinformation merged, thus elevating the problem to a whole new level and giving rise to the first global infodemic.
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
In particular, FANG yields significant improvements for the task of fake news detection, and it is robust in the case of limited training data.
Can We Spot the "Fake News" Before It Was Even Written?
Given the recent proliferation of disinformation online, there has been also growing research interest in automatically debunking rumors, false claims, and "fake news."
aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning
We describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles.
On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning
We believe that our rethinking of the Wasserstein-Procrustes problem could enable further research, thus helping to develop better algorithms for aligning word embeddings across languages.
Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms
With the outbreak of the COVID-19 pandemic, people turned to social media to read and to share timely information including statistics, warnings, advice, and inspirational stories.
Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media
The first four tasks compose the full pipeline of claim verification in social media: Task 1 on check-worthiness estimation, Task 2 on retrieving previously fact-checked claims, Task 3 on evidence retrieval, and Task 4 on claim verification.
A Survey on Computational Propaganda Detection
Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda.
Predicting the Topical Stance and Political Leaning of Media using Tweets
Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers.
SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)
We present the results and main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2020).
That is a Known Lie: Detecting Previously Fact-Checked Claims
Interestingly, despite the importance of the task, it has been largely ignored by the research community so far.
Prta: A System to Support the Analysis of Propaganda Techniques in the News
However, little attention has been paid to the specific rhetorical and psychological techniques used to convey propaganda messages.
What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context
Alternatively, we can profile entire news outlets and look for those that are likely to publish fake or biased content.
Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection
Recently, the advances in pre-trained language models, namely contextualized models such as ELMo and BERT have revolutionized the field by tapping the potential of training very large models with just a few steps of fine-tuning on a task-specific dataset.
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic.
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification
The widespread use of offensive content in social media has led to an abundance of research in detecting language such as hate speech, cyberbullying, and cyber-aggression.
On the Effect of Dropping Layers of Pre-trained Transformer Models
Transformer-based NLP models are trained using hundreds of millions or even billions of parameters, limiting their applicability in computationally constrained environments.
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks.
CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media
Finally, the lab offers a fifth task that asks to predict the check-worthiness of the claims made in English political debates and speeches.
A Context-Aware Approach for Detecting Check-Worthy Claims in Political Debates
In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking.
Proppy: A System to Unmask Propaganda in Online News
We present proppy, the first publicly available real-world, real-time propaganda detection system for online news, which aims at raising awareness, thus potentially limiting the impact of propaganda and helping fight disinformation.
SemEval-2013 Task 2: Sentiment Analysis in Twitter
To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a message-level subtask.