3 code implementations • 7 Sep 2020 • Alex Nikolov, Giovanni Da San Martino, Ivan Koychev, Preslav Nakov
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.
1 code implementation • 22 Jan 2022 • Kristiyan Vachev, Momchil Hardalov, Georgi Karadzhov, Georgi Georgiev, Ivan Koychev, Preslav Nakov
Testing with quiz questions has proven to be an effective way to assess and improve the educational process.
4 code implementations • 8 Apr 2020 • Hassan Sajjad, Fahim Dalvi, Nadir Durrani, Preslav Nakov
Transformer-based NLP models are trained using hundreds of millions or even billions of parameters, limiting their applicability in computationally constrained environments.
1 code implementation • 25 Aug 2023 • Yuxia Wang, Haonan Li, Xudong Han, Preslav Nakov, Timothy Baldwin
With the rapid evolution of large language models (LLMs), new and hard-to-predict harmful capabilities are emerging.
1 code implementation • 18 Aug 2020 • Van-Hoang Nguyen, Kazunari Sugiyama, Preslav Nakov, Min-Yen Kan
In particular, FANG yields significant improvements for the task of fake news detection, and it is robust in the case of limited training data.
1 code implementation • 11 Dec 2023 • Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Xuguang Ren, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Tim Baldwin, Eric P. Xing
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers.
2 code implementations • 24 May 2023 • Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Chenxi Whitehouse, Osama Mohammed Afzal, Tarek Mahmoud, Toru Sasaki, Thomas Arnold, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov
These results show that the problem is far from solved and that there is a lot of room for improvement.
1 code implementation • 1 Feb 2023 • Muhammad Arslan Manzoor, Sarah Albarri, Ziting Xian, Zaiqiao Meng, Preslav Nakov, Shangsong Liang
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.
2 code implementations • EMNLP 2018 • Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, Preslav Nakov
We present a study on predicting the factuality of reporting and bias of news media.
1 code implementation • ACL 2020 • Ramy Baly, Georgi Karadzhov, Jisun An, Haewoon Kwak, Yoan Dinkov, Ahmed Ali, James Glass, Preslav Nakov
Alternatively, we can profile entire news outlets and look for those that are likely to publish fake or biased content.
1 code implementation • 22 May 2023 • Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu, William Yang Wang, Min-Yen Kan, Preslav Nakov
Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning.
1 code implementation • EMNLP 2020 • Ramy Baly, Giovanni Da San Martino, James Glass, Preslav Nakov
We explore the task of predicting the leading political ideology or bias of news articles.
2 code implementations • EMNLP 2020 • Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova, Yoan Dinkov, Ivan Koychev, Preslav Nakov
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.
1 code implementation • 9 May 2022 • Shivam Sharma, Firoj Alam, Md. Shad Akhtar, Dimitar Dimitrov, Giovanni Da San Martino, Hamed Firooz, Alon Halevy, Fabrizio Silvestri, Preslav Nakov, Tanmoy Chakraborty
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.
1 code implementation • 15 Nov 2023 • Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov
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.
1 code implementation • SEMEVAL 2020 • Anton Chernyavskiy, Dmitry Ilvovsky, Preslav Nakov
We describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles.
1 code implementation • SEMEVAL 2021 • Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino
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.
2 code implementations • ACL 2020 • Shaden Shaar, Giovanni Da San Martino, Nikolay Babulkov, Preslav Nakov
Interestingly, despite the importance of the task, it has been largely ignored by the research community so far.
1 code implementation • EMNLP 2018 • Darsh J Shah, Tao Lei, Alessandro Moschitti, Salvatore Romeo, Preslav Nakov
We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions.
1 code implementation • 10 Mar 2022 • Kung-Hsiang Huang, Kathleen McKeown, Preslav Nakov, Yejin Choi, Heng Ji
Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation.
3 code implementations • 21 Jan 2020 • Alberto Barron-Cedeno, Tamer Elsayed, Preslav Nakov, Giovanni Da San Martino, Maram Hasanain, Reem Suwaileh, Fatima Haouari
Finally, the lab offers a fifth task that asks to predict the check-worthiness of the claims made in English political debates and speeches.
3 code implementations • 15 Jul 2020 • Alberto Barron-Cedeno, Tamer Elsayed, Preslav Nakov, Giovanni Da San Martino, Maram Hasanain, Reem Suwaileh, Fatima Haouari, Nikolay Babulkov, Bayan Hamdan, Alex Nikolov, Shaden Shaar, Zien Sheikh Ali
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.
1 code implementation • EMNLP 2021 • Mohammed Saeed, Naser Ahmadi, Preslav Nakov, Paolo Papotti
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.
2 code implementations • 16 Dec 2021 • Revanth Gangi Reddy, Sai Chetan, Zhenhailong Wang, Yi R. Fung, Kathryn Conger, Ahmed Elsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji
In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain.
1 code implementation • Findings (EMNLP) 2021 • Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
We focus on two tasks: (i)detecting harmful memes, and (ii)identifying the social entities they target.
1 code implementation • 5 Nov 2022 • Zihui Gu, Ju Fan, Nan Tang, Preslav Nakov, Xiaoman Zhao, Xiaoyong Du
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
1 code implementation • 23 May 2023 • Jinyan Su, Terry Yue Zhuo, Di Wang, Preslav Nakov
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.
1 code implementation • 13 Sep 2021 • Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein
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.
1 code implementation • ACL 2021 • Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino
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.
1 code implementation • 22 May 2023 • Xinyuan Lu, Liangming Pan, Qian Liu, Preslav Nakov, Min-Yen Kan
Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence.
1 code implementation • RANLP 2017 • Pepa Gencheva, Preslav Nakov, Llu{\'\i}s M{\`a}rquez, Alberto Barr{\'o}n-Cede{\~n}o, Ivan Koychev
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.
2 code implementations • 3 Apr 2019 • Kareem Darwish, Peter Stefanov, Michaël J. Aupetit, Preslav Nakov
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
1 code implementation • EMNLP 2021 • Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein
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.
1 code implementation • 15 Jul 2020 • Firoj Alam, Fahim Dalvi, Shaden Shaar, Nadir Durrani, Hamdy Mubarak, Alex Nikolov, Giovanni Da San Martino, Ahmed Abdelali, Hassan Sajjad, Kareem Darwish, Preslav Nakov
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.
1 code implementation • 11 Oct 2023 • Liangming Pan, Xinyuan Lu, Min-Yen Kan, Preslav Nakov
Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them.
1 code implementation • 27 Oct 2023 • Shubham Mittal, Megha Sundriyal, Preslav Nakov
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.
1 code implementation • SEMEVAL 2016 • Todor Mihaylov, Preslav Nakov
We describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering.
1 code implementation • SEMEVAL 2017 • Preslav Nakov, Doris Hoogeveen, Lluís Màrquez, Alessandro Moschitti, Hamdy Mubarak, Timothy Baldwin, Karin Verspoor
We describe SemEval-2017 Task 3 on Community Question Answering.
1 code implementation • 20 Feb 2024 • Fajri Koto, Haonan Li, Sara Shatnawi, Jad Doughman, Abdelrahman Boda Sadallah, Aisha Alraeesi, Khalid Almubarak, Zaid Alyafeai, Neha Sengupta, Shady Shehata, Nizar Habash, Preslav Nakov, Timothy Baldwin
The focus of language model evaluation has transitioned towards reasoning and knowledge-intensive tasks, driven by advancements in pretraining large models.
2 code implementations • 12 Jul 2017 • Georgi Karadjov, Tsvetomila Mihaylova, Yasen Kiprov, Georgi Georgiev, Ivan Koychev, Preslav Nakov
Users posting online expect to remain anonymous unless they have logged in, which is often needed for them to be able to discuss freely on various topics.
3 code implementations • 8 Mar 2018 • Tsvetomila Mihaylova, Preslav Nakov, Lluis Marquez, Alberto Barron-Cedeno, Mitra Mohtarami, Georgi Karadzhov, James Glass
Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information.
1 code implementation • 4 Aug 2019 • Pepa Atanasova, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, Georgi Karadzhov, Tsvetomila Mihaylova, Mitra Mohtarami, James Glass
We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information.
2 code implementations • Findings (EMNLP) 2021 • Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov
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.
1 code implementation • 23 May 2023 • Yikang Pan, Liangming Pan, Wenhu Chen, Preslav Nakov, Min-Yen Kan, William Yang Wang
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.
2 code implementations • NAACL 2019 • Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, Ritesh Kumar
In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media.
1 code implementation • 20 Oct 2019 • Yoan Dinkov, Ahmed Ali, Ivan Koychev, Preslav Nakov
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.
1 code implementation • RANLP 2017 • Georgi Karadzhov, Preslav Nakov, Lluis Marquez, Alberto Barron-Cedeno, Ivan Koychev
Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims.
1 code implementation • RANLP 2019 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc.
1 code implementation • 19 Nov 2019 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
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.
2 code implementations • 4 Jun 2023 • Momchil Hardalov, Pepa Atanasova, Todor Mihaylov, Galia Angelova, Kiril Simov, Petya Osenova, Ves Stoyanov, Ivan Koychev, Preslav Nakov, Dragomir Radev
We run the first systematic evaluation of pre-trained language models for Bulgarian, comparing and contrasting results across the nine tasks in the benchmark.
1 code implementation • 10 Mar 2018 • Georgi Karadzhov, Pepa Gencheva, Preslav Nakov, Ivan Koychev
So, we did this research on fake news/click-bait detection and trust us, it is totally great research, it really is!
1 code implementation • RANLP 2017 • Georgi Karadzhov, Pepa Gencheva, Preslav Nakov, Ivan Koychev
And we have totally tested it, trust us!
1 code implementation • 18 Jul 2020 • Guillem Ramírez, Rumen Dangovski, Preslav Nakov, Marin Soljačić
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.
2 code implementations • SEMEVAL 2019 • Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, Ritesh Kumar
We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval).
1 code implementation • 10 Oct 2022 • Momchil Hardalov, Anton Chernyavskiy, Ivan Koychev, Dmitry Ilvovsky, Preslav Nakov
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.
1 code implementation • 31 Oct 2022 • Shubham Mittal, Preslav Nakov
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.
1 code implementation • 11 Nov 2023 • Luke Bates, Peter Ebert Christensen, Preslav Nakov, Iryna Gurevych
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.
1 code implementation • COLING 2016 • Dame Jovanoski, Veno Pachovski, Preslav Nakov
Sentiment polarity lexicons are key resources for sentiment analysis, and researchers have invested a lot of efforts in their manual creation.
1 code implementation • Findings (NAACL) 2022 • Shaden Shaar, Firoj Alam, Giovanni Da San Martino, Preslav Nakov
Recent years have seen the proliferation of disinformation and fake news online.
1 code implementation • NAACL (NLP4IF) 2021 • Tariq Alhindi, Amal Alabdulkarim, Ali Alshehri, Muhammad Abdul-Mageed, Preslav Nakov
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.
1 code implementation • 2 Sep 2018 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
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.
1 code implementation • RANLP 2019 • Yoan Dinkov, Ivan Koychev, Preslav Nakov
Online media aim for reaching ever bigger audience and for attracting ever longer attention span.
2 code implementations • IJCNLP 2019 • Prathyusha Jwalapuram, Shafiq Joty, Irina Temnikova, Preslav Nakov
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.
1 code implementation • CONSTRAINT (ACL) 2022 • Rabindra Nath Nandi, Firoj Alam, Preslav Nakov
The content that is posted and shared online can be textual, visual, or a combination of both, e. g., in a meme.
1 code implementation • 22 Oct 2023 • Megha Sundriyal, Tanmoy Chakraborty, Preslav Nakov
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.
1 code implementation • 15 Mar 2024 • Rocktim Jyoti Das, Simeon Emilov Hristov, Haonan Li, Dimitar Iliyanov Dimitrov, Ivan Koychev, Preslav Nakov
Solving the problems in the dataset requires advanced perception and joint reasoning over the text and the visual content of the image.
1 code implementation • 20 Nov 2020 • Ileana Rugina, Rumen Dangovski, Li Jing, Preslav Nakov, Marin Soljačić
The attention mechanism is a key component of the neural revolution in Natural Language Processing (NLP).
1 code implementation • 14 Sep 2021 • Shaden Shaar, Nikola Georgiev, Firoj Alam, Giovanni Da San Martino, Aisha Mohamed, Preslav Nakov
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.
1 code implementation • Findings (NAACL) 2022 • Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
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.
1 code implementation • 23 May 2023 • Muhammad Umar Salman, Asif Hanif, Shady Shehata, Preslav Nakov
Yet, it is common to find a mix of multiple languages in social media communication, a phenomenon known as code-switching.
1 code implementation • 23 Jan 2024 • Iman Munire Bilal, Preslav Nakov, Rob Procter, Maria Liakata
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.
no code implementations • NAACL 2018 • Ramy Baly, Mitra Mohtarami, James Glass, Lluis Marquez, Alessandro Moschitti, Preslav Nakov
A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e. g., news websites, social media, etc.
no code implementations • NAACL 2018 • Israa Jaradat, Pepa Gencheva, Alberto Barron-Cedeno, Lluis Marquez, Preslav Nakov
We present ClaimRank, an online system for detecting check-worthy claims.
no code implementations • NAACL 2018 • Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluis Marquez, Alessandro Moschitti
We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction.
Ranked #6 on Fake News Detection on FNC-1
no code implementations • 5 Oct 2017 • Francisco Guzmán, Shafiq R. Joty, Lluís Màrquez, Preslav Nakov
We present a 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.
no code implementations • CL 2017 • Shafiq Joty, Francisco Guzmán, Lluís Màrquez, Preslav Nakov
In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation.
no code implementations • 4 Oct 2017 • Preslav Nakov
Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e. g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world.
no code implementations • 4 Oct 2017 • Giovanni Da San Martino, Salvatore Romeo, Alberto Barron-Cedeno, Shafiq Joty, Lluis Marquez, Alessandro Moschitti, Preslav Nakov
We compare a kernel-based system with a feed-forward neural network in a scenario where a large parallel corpus is available for training a machine translation system, bilingual dictionaries, and cross-language word embeddings.
no code implementations • RANLP 2017 • Martin Boyanov, Ivan Koychev, Preslav Nakov, Alessandro Moschitti, Giovanni Da San Martino
Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbots.
no code implementations • RANLP 2017 • Preslav Nakov, Stephan Vogel
We explore the idea of automatically crafting a tuning dataset for Statistical Machine Translation (SMT) that makes the hyper-parameters of the SMT system more robust with respect to some specific deficiencies of the parameter tuning algorithms.
no code implementations • 21 Sep 2017 • Ahmed Ali, Preslav Nakov, Peter Bell, Steve Renals
We study the problem of evaluating automatic speech recognition (ASR) systems that target dialectal speech input.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 20 Jul 2017 • Todor Mihaylov, Daniel Belchev, Yasen Kiprov, Ivan Koychev, Preslav Nakov
This leads us to the idea to build a good/bad polarity lexicon as an analogy to the positive/negative sentiment polarity lexicons, commonly used in sentiment analysis.
no code implementations • 21 Jun 2017 • Shafiq Joty, Preslav Nakov, Lluís Màrquez, Israa Jaradat
We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language.
no code implementations • 9 Oct 2016 • Ahmad Musleh, Nadir Durrani, Irina Temnikova, Preslav Nakov, Stephan Vogel, Osama Alsaad
We present research towards bridging the language gap between migrant workers in Qatar and medical staff.
no code implementations • 5 Sep 2016 • Fahad Al-Obaidli, Stephen Cox, Preslav Nakov
In particular, we look at movie subtitles as a unique, rich resource, as subtitles in one language often get translated into other languages.
no code implementations • 18 Jun 2016 • Hassan Sajjad, Nadir Durrani, Francisco Guzman, Preslav Nakov, Ahmed Abdelali, Stephan Vogel, Wael Salloum, Ahmed El Kholy, Nizar Habash
The competition focused on informal dialectal Arabic, as used in SMS, chat, and speech.
no code implementations • EMNLP 2018 • Shafiq Joty, Lluis Marquez, Preslav Nakov
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.
no code implementations • CONLL 2017 • Shafiq Joty, Preslav Nakov, Llu{\'\i}s M{\`a}rquez, Israa Jaradat
We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language.
no code implementations • COLING 2018 • Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Ahmed Ali, Suwon Shon, James Glass, Yves Scherrer, Tanja Samard{\v{z}}i{\'c}, Nikola Ljube{\v{s}}i{\'c}, J{\"o}rg Tiedemann, Chris van der Lee, Stefan Grondelaers, Nelleke Oostdijk, Dirk Speelman, Antal Van den Bosch, Ritesh Kumar, Bornini Lahiri, Mayank Jain
We present the results and the findings of the Second VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects.
no code implementations • WS 2017 • Marcos Zampieri, Shervin Malmasi, Nikola Ljube{\v{s}}i{\'c}, Preslav Nakov, Ahmed Ali, J{\"o}rg Tiedemann, Yves Scherrer, No{\"e}mi Aepli
We present the results of the VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects, which we organized as part of the fourth edition of the VarDial workshop at EACL{'}2017.
no code implementations • WS 2017 • Sharid Lo{\'a}iciga, Sara Stymne, Preslav Nakov, Christian Hardmeier, J{\"o}rg Tiedemann, Mauro Cettolo, Yannick Versley
We describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction.
no code implementations • WS 2016 • Shervin Malmasi, Marcos Zampieri, Nikola Ljube{\v{s}}i{\'c}, Preslav Nakov, Ahmed Ali, J{\"o}rg Tiedemann
We present the results of the third edition of the Discriminating between Similar Languages (DSL) shared task, which was organized as part of the VarDial{'}2016 workshop at COLING{'}2016.
no code implementations • WS 2016 • Preslav Nakov
In particular, I will demonstrate how contemporary MT systems fail on them, and I will discuss some possible solutions.
no code implementations • COLING 2016 • Enamul Hoque, Shafiq Joty, Llu{\'\i}s M{\`a}rquez, Alberto Barr{\'o}n-Cede{\~n}o, Giovanni Da San Martino, Aless Moschitti, ro, Preslav Nakov, Salvatore Romeo, Giuseppe Carenini
We present an interactive system to provide effective and efficient search capabilities in Community Question Answering (cQA) forums.
1 code implementation • RANLP 2017 • Preslav Nakov, Tsvetomila Mihaylova, Llu{\'\i}s M{\`a}rquez, Yashkumar Shiroya, Ivan Koychev
We address information credibility in community forums, in a setting in which the credibility of an answer posted in a question thread by a particular user has to be predicted.
no code implementations • SEMEVAL 2015 • Massimo Nicosia, Simone Filice, Alberto Barr{\'o}n-Cede{\~n}o, Iman Saleh, Hamdy Mubarak, Wei Gao, Preslav Nakov, Giovanni Da San Martino, Aless Moschitti, ro, Kareem Darwish, Llu{\'\i}s M{\`a}rquez, Shafiq Joty, Walid Magdy
no code implementations • 12 Feb 2019 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
Recent advances in deep neural networks, language modeling and language generation have introduced new ideas to the field of conversational agents.
no code implementations • NAACL 2019 • Ramy Baly, Georgi Karadzhov, Abdelrhman Saleh, James Glass, Preslav Nakov
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.
no code implementations • SEMEVAL 2019 • Abdelrhman Saleh, Ramy Baly, Alberto Barrón-Cedeño, Giovanni Da San Martino, Mitra Mohtarami, Preslav Nakov, James Glass
In this paper, we describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection.
no code implementations • 25 May 2019 • Pepa Atanasova, Georgi Karadzhov, Yasen Kiprov, Preslav Nakov, Fabrizio Sebastiani
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.
no code implementations • SEMEVAL 2019 • Tsvetomila Mihaylova, Georgi Karadjov, Pepa Atanasova, Ramy Baly, Mitra Mohtarami, Preslav Nakov
For subtask A, all systems improved over the majority class baseline.
no code implementations • NAACL 2019 • Nadir Durrani, Fahim Dalvi, Hassan Sajjad, Yonatan Belinkov, Preslav Nakov
Recent work has shown that contextualized word representations derived from neural machine translation are a viable alternative to such from simple word predictions tasks.
no code implementations • SEMEVAL 2019 • Daniel Shaprin, Giovanni Da San Martino, Alberto Barr{\'o}n-Cede{\~n}o, Preslav Nakov
We describe the system submitted by the Jack Ryder team to SemEval-2019 Task 4 on Hyperpartisan News Detection.
no code implementations • TACL 2019 • Rumen Dangovski, Li Jing, Preslav Nakov, Mi{\'c}o Tatalovi{\'c}, Marin Solja{\v{c}}i{\'c}
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.
no code implementations • 17 Jun 2019 • Daniel Kopev, Dimitrina Zlatkova, Kristiyan Mitov, Atanas Atanasov, Momchil Hardalov, Ivan Koychev, Preslav Nakov
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.
no code implementations • 2 Jul 2019 • Peter Stefanov, Kareem Darwish, Atanas Atanasov, Preslav Nakov
Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers.
no code implementations • RANLP 2019 • Slavena Vasileva, Pepa Atanasova, Lluís Màrquez, Alberto Barrón-Cedeño, Preslav Nakov
We propose a multi-task deep-learning approach for estimating the check-worthiness of claims in political debates.
no code implementations • RANLP 2019 • Lilia Simeonova, Kiril Simov, Petya Osenova, Preslav Nakov
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.
1 code implementation • IJCNLP 2019 • Dimitrina Zlatkova, Preslav Nakov, Ivan Koychev
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.
no code implementations • 4 Oct 2019 • Daniel Kopev, Ahmed Ali, Ivan Koychev, Preslav Nakov
We present work on deception detection, where, given a spoken claim, we aim to predict its factuality.
1 code implementation • CONLL 2019 • Atanas Atanasov, Gianmarco De Francisci Morales, Preslav Nakov
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.
no code implementations • IJCNLP 2019 • Yifan Zhang, Giovanni Da San Martino, Alberto Barrón-Cedeño, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, Kareem Darwish, James Glass, Preslav Nakov
We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what's behind a news story.
no code implementations • 6 Oct 2019 • Giovanni Da San Martino, Seunghak Yu, Alberto Barrón-Cedeño, Rostislav Petrov, Preslav Nakov
Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda.
no code implementations • IJCNLP 2019 • Mitra Mohtarami, James Glass, Preslav Nakov
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.
no code implementations • WS 2019 • Giovanni Da San Martino, Alberto Barrón-Cedeño, Preslav Nakov
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).
no code implementations • IJCNLP 2019 • Giovanni Da San Martino, Seunghak Yu, Alberto Barr{\'o}n-Cede{\~n}o, Rostislav Petrov, Preslav Nakov
Propaganda aims at influencing people{'}s mindset with the purpose of advancing a specific agenda.
no code implementations • 15 Nov 2019 • Seunghak Yu, Giovanni Da San Martino, Preslav Nakov
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.
no code implementations • ACL 2016 • Todor Mihaylov, Preslav Nakov
There are different definitions of what a troll is.
no code implementations • 19 Nov 2019 • Minh-Thang Luong, Preslav Nakov, Min-Yen Kan
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.
no code implementations • 20 Nov 2019 • Preslav Nakov
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.
no code implementations • EMNLP 2015 • Shafiq Joty, Alberto Barrón-Cedeño, Giovanni Da San Martino, Simone Filice, Lluís Màrquez, Alessandro Moschitti, Preslav Nakov
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.
no code implementations • 23 Nov 2019 • Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, Stan Szpakowicz
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.
no code implementations • SEMEVAL 2013 • Iris Hendrickx, Preslav Nakov, Stan Szpakowicz, Zornitsa Kozareva, Diarmuid Ó Séaghdha, Tony Veale
In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results.
no code implementations • EACL 2012 • Georgi Georgiev, Valentin Zhikov, Petya Osenova, Kiril Simov, Preslav Nakov
We present experiments with part-of-speech tagging for Bulgarian, a Slavic language with rich inflectional and derivational morphology.
no code implementations • SEMEVAL 2015 • Preslav Nakov, Lluís Màrquez, Walid Magdy, Alessandro Moschitti, James Glass, Bilal Randeree
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.
no code implementations • WS 2016 • Liane Guillou, Christian Hardmeier, Preslav Nakov, Sara Stymne, Jörg Tiedemann, Yannick Versley, Mauro Cettolo, Bonnie Webber, Andrei Popescu-Belis
We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction.
no code implementations • 27 Nov 2019 • Su Nam Kim, Preslav Nakov
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.
no code implementations • WS 2014 • Shafiq Joty, Francisco Guzman, Lluis Marquez, Preslav Nakov
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.
no code implementations • 28 Nov 2019 • Veselin Raychev, Preslav Nakov
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.
no code implementations • SEMEVAL 2017 • Sara Rosenthal, Noura Farra, Preslav Nakov
This paper describes the fifth year of the Sentiment Analysis in Twitter task.
no code implementations • 23 Nov 2019 • Preslav Nakov
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.
no code implementations • 4 Dec 2019 • Evgeni Stefchov, Galia Angelova, Preslav Nakov
We present the construction of an annotated corpus of PubMed abstracts reporting about positive, negative or neutral effects of treatments or substances.
no code implementations • SEMEVAL 2016 • Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, Veselin Stoyanov
The three new subtasks focus on two variants of the basic ``sentiment classification in Twitter'' task.
no code implementations • SEMEVAL 2016 • Preslav Nakov, Lluís Màrquez, Alessandro Moschitti, Walid Magdy, Hamdy Mubarak, Abed Alhakim Freihat, James Glass, Bilal Randeree
This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic.
no code implementations • SEMEVAL 2015 • Sara Rosenthal, Saif M. Mohammad, Preslav Nakov, Alan Ritter, Svetlana Kiritchenko, Veselin Stoyanov
In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter.
no code implementations • ACL 2016 • Francisco Guzmán, Lluís Màrquez, Preslav Nakov
We explore the applicability of machine translation evaluation (MTE) methods to a very different problem: answer ranking in community Question Answering.
no code implementations • SEMEVAL 2014 • Sara Rosenthal, Preslav Nakov, Alan Ritter, Veselin Stoyanov
We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014.
no code implementations • IJCNLP 2015 • Francisco Guzman, Shafiq Joty, Lluis Marquez, Preslav Nakov
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.
no code implementations • 14 Dec 2019 • Alberto Barrón-Cedeño, Giovanni Da San Martino, Israa Jaradat, Preslav Nakov
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.
no code implementations • SEMEVAL 2013 • Preslav Nakov, Zornitsa Kozareva, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, Theresa Wilson
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.
no code implementations • 14 Dec 2019 • Pepa Gencheva, Ivan Koychev, Lluís Màrquez, Alberto Barrón-Cedeño, Preslav Nakov
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.
no code implementations • 27 Feb 2020 • Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett
Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks.
no code implementations • 30 Apr 2020 • Momchil Hardalov, Ivan Koychev, Preslav Nakov
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.
no code implementations • Findings (ACL) 2021 • Sara Rosenthal, Pepa Atanasova, Georgi Karadzhov, Marcos Zampieri, Preslav Nakov
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.
no code implementations • ACL 2020 • Giovanni Da San Martino, Shaden Shaar, Yifan Zhang, Seunghak Yu, Alberto Barrón-Cedeño, Preslav Nakov
However, little attention has been paid to the specific rhetorical and psychological techniques used to convey propaganda messages.
no code implementations • SEMEVAL 2020 • Marcos Zampieri, Preslav Nakov, Sara Rosenthal, Pepa Atanasova, Georgi Karadzhov, Hamdy Mubarak, Leon Derczynski, Zeses Pitenis, Çağrı Çöltekin
We present the results and main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2020).
no code implementations • ACL 2020 • Peter Stefanov, Kareem Darwish, Atanas Atanasov, Preslav Nakov
Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers.
no code implementations • 15 Jul 2020 • Giovanni Da San Martino, Stefano Cresci, Alberto Barron-Cedeno, Seunghak Yu, Roberto Di Pietro, Preslav Nakov
Propaganda campaigns aim at influencing people's mindset with the purpose of advancing a specific agenda.
no code implementations • 10 Aug 2020 • Preslav Nakov
Given the recent proliferation of disinformation online, there has been also growing research interest in automatically debunking rumors, false claims, and "fake news."
no code implementations • EMNLP 2020 • Matthew Khoury, Rumen Dangovski, Longwu Ou, Preslav Nakov, Yichen Shen, Li Jing
To address this issue, we propose a novel vector-vector-matrix architecture (VVMA), which greatly reduces the latency at inference time for NMT.
no code implementations • 27 Feb 2021 • Arnav Arora, Preslav Nakov, Momchil Hardalov, Sheikh Muhammad Sarwar, Vibha Nayak, Yoan Dinkov, Dimitrina Zlatkova, Kyle Dent, Ameya Bhatawdekar, Guillaume Bouchard, Isabelle Augenstein
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.
no code implementations • Findings (NAACL) 2022 • Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein
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).
no code implementations • 13 Mar 2021 • Preslav Nakov, David Corney, Maram Hasanain, Firoj Alam, Tamer Elsayed, Alberto Barrón-Cedeño, Paolo Papotti, Shaden Shaar, Giovanni Da San Martino
The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism.
no code implementations • 16 Mar 2021 • Preslav Nakov, Husrev Taha Sencar, Jisun An, Haewoon Kwak
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.
no code implementations • COLING 2022 • Firoj Alam, Stefano Cresci, Tanmoy Chakraborty, Fabrizio Silvestri, Dimiter Dimitrov, Giovanni Da San Martino, Shaden Shaar, Hamed Firooz, Preslav Nakov
As a result, researchers started leveraging different modalities and combinations thereof to tackle online multimodal offensive content.
no code implementations • 31 Mar 2021 • Sheikh Muhammad Sarwar, Dimitrina Zlatkova, Momchil Hardalov, Yoan Dinkov, Isabelle Augenstein, Preslav Nakov
The framework is based on a nearest-neighbour architecture.
no code implementations • 9 Apr 2021 • Anton Chernyavskiy, Dmitry Ilvovsky, Preslav Nakov
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.
no code implementations • 16 Apr 2021 • Anton Chernyavskiy, Dmitry Ilvovsky, Preslav Nakov
The rise of Internet has made it a major source of information.
no code implementations • SEMEVAL 2020 • Giovanni Da San Martino, Alberto Barr{\'o}n-Cede{\~n}o, Henning Wachsmuth, Rostislav Petrov, Preslav Nakov
We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles.
no code implementations • EMNLP 2020 • Preslav Nakov, Giovanni Da San Martino
The rise of social media has democratized content creation and has made it easy for everybody to share and spread information online.
no code implementations • RANLP 2021 • Krasimira Bozhanova, Yoan Dinkov, Ivan Koychev, Maria Castaldo, Tommaso Venturini, Preslav Nakov
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.
no code implementations • RANLP 2021 • Kristiyan Vachev, Momchil Hardalov, Georgi Karadzhov, Georgi Georgiev, Ivan Koychev, Preslav Nakov
In education, open-ended quiz questions have become an important tool for assessing the knowledge of students.
no code implementations • RANLP 2021 • Seunghak Yu, Giovanni Da San Martino, Mitra Mohtarami, James Glass, Preslav Nakov
Online users today are exposed to misleading and propagandistic news articles and media posts on a daily basis.
no code implementations • RANLP 2021 • Preslav Nakov, Firoj Alam, Shaden Shaar, Giovanni Da San Martino, Yifan Zhang
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.
no code implementations • NAACL (NLP4IF) 2021 • Shaden Shaar, Firoj Alam, Giovanni Da San Martino, Alex Nikolov, Wajdi Zaghouani, Preslav Nakov, Anna Feldman
Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used.
no code implementations • 23 Sep 2021 • Preslav Nakov, Giovanni Da San Martino, Tamer Elsayed, Alberto Barrón-Cedeño, Rubén Míguez, Shaden Shaar, Firoj Alam, Fatima Haouari, Maram Hasanain, Watheq Mansour, Bayan Hamdan, Zien Sheikh Ali, Nikolay Babulkov, Alex Nikolov, Gautam Kishore Shahi, Julia Maria Struß, Thomas Mandl, Mucahid Kutlu, Yavuz Selim Kartal
We describe the fourth edition of the CheckThat!
no code implementations • 27 Sep 2021 • Kristina Hristakieva, Stefano Cresci, Giovanni Da San Martino, Mauro Conti, Preslav Nakov
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.
no code implementations • RANLP 2013 • Jörg Tiedemann, Preslav Nakov
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.
no code implementations • 27 Sep 2021 • Preslav Nakov, Hwee Tou Ng
We propose a novel approach to translating from a morphologically complex language.
no code implementations • RANLP 2015 • Dame Jovanoski, Veno Pachovski, Preslav Nakov
We present work on sentiment analysis in Twitter for Macedonian.
no code implementations • RANLP 2015 • Todor Mihaylov, Ivan Koychev, Georgi Georgiev, Preslav Nakov
Recently, Web forums have been invaded by opinion manipulation trolls.
no code implementations • 26 Sep 2021 • Georgi Georgiev, Preslav Nakov, Kuzman Ganchev, Petya Osenova, Kiril Ivanov Simov
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.
no code implementations • SemEval (ACL) 2016 • Tsvetomila Mihaylova, Pepa Gencheva, Martin Boyanov, Ivana Yovcheva, Todor Mihaylov, Momchil Hardalov, Yasen Kiprov, Daniel Balchev, Ivan Koychev, Preslav Nakov, Ivelina Nikolova, Galia Angelova
We present the system we built for participating in SemEval-2016 Task 3 on Community Question Answering.