Search Results for author:
Found 226 papers, 73 papers with code
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.
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.
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-GPT: End-to-End Fine-Grained Document-Level Fact-Checking and Correction of LLM Output
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 Language Model Confidence Estimation and Calibration
In particular, we discuss methods and techniques for LM confidence estimation and calibration, encompassing different LMs and various tasks.
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
In this study, we aim to rethink STS and NLI in the era of large language models (LLMs).
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
A large portion of society united around the same vision and ideas carries enormous energy.
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
In this work, we aim to develop automatic systems to identify machine-generated text and to detect potential misuse.
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.
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.
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.
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.
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.
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.
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).
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.
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.
Exposing Paid Opinion Manipulation Trolls
Recently, Web forums have been invaded by opinion manipulation trolls.
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.
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.
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!
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.
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.
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.
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.
Interpretable Propaganda Detection in News Articles
Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis.
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.
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).
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.
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
The attention mechanism is a key component of the neural revolution in 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.
A Survey on Computational Propaganda Detection
Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda.
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.
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).
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.
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.
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.
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.
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-2014 Task 9: Sentiment Analysis in Twitter
We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014.
Machine Translation Evaluation Meets Community Question Answering
We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering.
Pairwise Neural Machine Translation Evaluation
We present a novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation.
SemEval-2015 Task 10: Sentiment Analysis in Twitter
In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter.
Towards Constructing a Corpus for Studying the Effects of Treatments and Substances Reported in PubMed Abstracts
We present the construction of an annotated corpus of PubMed abstracts reporting about positive, negative or neutral effects of treatments or substances.
SemEval-2016 Task 3: Community Question Answering
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic.
SemEval-2016 Task 4: Sentiment Analysis in Twitter
The three new subtasks focus on two variants of the basic ``sentiment classification in Twitter'' task.
SemEval-2017 Task 4: Sentiment Analysis in Twitter
This paper describes the fifth year of the Sentiment Analysis in Twitter task.
SemEval-2017 Task 3: Community Question Answering
We describe SemEval-2017 Task 3 on Community Question Answering.
DiscoTK: Using Discourse Structure for Machine Translation Evaluation
We present novel automatic metrics for machine translation evaluation that use discourse structure and convolution kernels to compare the discourse tree of an automatic translation with that of the human reference.
Language-Independent Sentiment Analysis Using Subjectivity and Positional Information
We describe a novel language-independent approach to the task of determining the polarity, positive or negative, of the author's opinion on a specific topic in natural language text.
Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction
We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction.
Large-Scale Noun Compound Interpretation Using Bootstrapping and the Web as a Corpus
We employ bootstrapping and web statistics, and utilize the relationship between NCs and paraphrasing patterns to jointly extract NCs and such patterns in multiple alternating iterations.
SemEval-2015 Task 3: Answer Selection in Community Question Answering
Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e. g., the exploitation of the interaction between users and the structure of related posts.
Feature-Rich Part-of-speech Tagging for Morphologically Complex Languages: Application to Bulgarian
We present experiments with part-of-speech tagging for Bulgarian, a Slavic language with rich inflectional and derivational morphology.
SemEval-2013 Task 4: Free Paraphrases of Noun Compounds
In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results.
SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals
In this paper, we define the task, describe the creation of the datasets, and discuss the results of the participating 28 systems submitted by 10 teams.
Using the Web as an Implicit Training Set: Application to Noun Compound Syntax and Semantics
I address noun compound semantics by automatically generating paraphrasing verbs and prepositions that make explicit the hidden semantic relations between the nouns in a noun compound.
Paraphrasing Verbs for Noun Compound Interpretation
An important challenge for the automatic analysis of English written text is the abundance of noun compounds: sequences of nouns acting as a single noun.
Global Thread-Level Inference for Comment Classification in Community Question Answering
Community question answering, a recent evolution of question answering in the Web context, allows a user to quickly consult the opinion of a number of people on a particular topic, thus taking advantage of the wisdom of the crowd.
SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings
We describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering.
A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages
We propose a language-independent approach for improving statistical machine translation for morphologically rich languages using a hybrid morpheme-word representation where the basic unit of translation is the morpheme, but word boundaries are respected at all stages of the translation process.
In Search of Credible News
As this is an understudied problem, especially for languages other than English, we first collect and release to the research community three new balanced credible vs. fake news datasets derived from four online sources.
Hunting for Troll Comments in News Community Forums
There are different definitions of what a troll is.
Experiments in Detecting Persuasion Techniques in the News
Many recent political events, like the 2016 US Presidential elections or the 2018 Brazilian elections have raised the attention of institutions and of the general public on the role of Internet and social media in influencing the outcome of these events.
Fine-Grained Analysis of Propaganda in News Article
Propaganda aims at influencing people{'}s mindset with the purpose of advancing a specific agenda.
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection
FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific propaganda technique used in each such fragment (18-way classification task).
Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information
Our analysis shows that the use of acoustic signal helped to improve bias detection by more than 6% absolute over using text and metadata only.
Fine-Grained Analysis of Propaganda in News Articles
Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda.
Tanbih: Get To Know What You Are Reading
We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what's behind a news story.
Predicting the Role of Political Trolls in Social Media
In particular, we show how to classify trolls according to their political role ---left, news feed, right--- by using features extracted from social media, i. e., Twitter, in two scenarios: (i) in a traditional supervised learning scenario, where labels for trolls are available, and (ii) in a distant supervision scenario, where labels for trolls are not available, and we rely on more-commonly-available labels for news outlets mentioned by the trolls.
Detecting Deception in Political Debates Using Acoustic and Textual Features
We present work on deception detection, where, given a spoken claim, we aim to predict its factuality.
Contrastive Language Adaptation for Cross-Lingual Stance Detection
In particular, we introduce a novel contrastive language adaptation approach applied to memory networks, which ensures accurate alignment of stances in the source and target languages, and can effectively deal with the challenge of limited labeled data in the target language.
Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite
The ongoing neural revolution in machine translation has made it easier to model larger contexts beyond the sentence-level, which can potentially help resolve some discourse-level ambiguities such as pronominal anaphora, thus enabling better translations.
Fact-Checking Meets Fauxtography: Verifying Claims About Images
The recent explosion of false claims in social media and on the Web in general has given rise to a lot of manual fact-checking initiatives.
A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition
We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-of-speech (POS) tags, and morphological information.
Detecting Toxicity in News Articles: Application to Bulgarian
Online media aim for reaching ever bigger audience and for attracting ever longer attention span.
It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction
We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates.
Beyond English-Only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian
Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc.
Automatic Fact-Checking Using Context and Discourse Information
We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information.
Predicting the Topical Stance of Media and Popular Twitter Users
Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers.
Recursive Style Breach Detection with Multifaceted Ensemble Learning
We present a supervised approach for style change detection, which aims at predicting whether there are changes in the style in a given text document, as well as at finding the exact positions where such changes occur.
One Size Does Not Fit All: Comparing NMT Representations of Different Granularities
Recent work has shown that contextualized word representations derived from neural machine translation are a viable alternative to such from simple word predictions tasks.
Team Jack Ryder at SemEval-2019 Task 4: Using BERT Representations for Detecting Hyperpartisan News
We describe the system submitted by the Jack Ryder team to SemEval-2019 Task 4 on Hyperpartisan News Detection.
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums
For subtask A, all systems improved over the majority class baseline.
Evaluating Variable-Length Multiple-Option Lists in Chatbots and Mobile Search
While typically a user would expect a single response at any utterance, a system could also return multiple options for the user to select from, based on different system understandings of the user's intent.
Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection
In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection.
Unsupervised User Stance Detection on Twitter
We experiment with different combinations of user similarity features, dataset sizes, dimensionality reduction methods, and clustering algorithms to ascertain the most effective and most computationally efficient combinations across three different datasets (in English and Turkish).
Social and Information Networks 62P25, 91D30
Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media
In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles.
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)
We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval).
Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications
Stacking long short-term memory (LSTM) cells or gated recurrent units (GRUs) as part of a recurrent neural network (RNN) has become a standard approach to solving a number of tasks ranging from language modeling to text summarization.
Predicting the Type and Target of Offensive Posts in Social Media
In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media.
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots
Recent advances in deep neural networks, language modeling and language generation have introduced new ideas to the field of conversational agents.
Predicting Factuality of Reporting and Bias of News Media Sources
We present a study on predicting the factuality of reporting and bias of news media.
Joint Multitask Learning for Community Question Answering Using Task-Specific Embeddings
We address jointly two important tasks for Question Answering in community forums: given a new question, (i) find related existing questions, and (ii) find relevant answers to this new question.
Adversarial Domain Adaptation for Duplicate Question Detection
We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions.
Towards Automated Customer Support
Recent years have seen growing interest in conversational agents, such as chatbots, which are a very good fit for automated customer support because the domain in which they need to operate is narrow.
Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign
We present the results and the findings of the Second VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects.