Papers with Internet
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda (D19-50)
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| Challenge: | . - (EN) |
| Approach: | . - (EN) |
| Outcome: | . - (EN) |
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts (2022.acl-srw)
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| Challenge: | Sentiment analysis is a field that is growing due to the availability of the Internet and the growing number of online platforms. |
| Approach: | They propose an annotated Turkish dataset suitable for targeted sentiment analysis. |
| Outcome: | The proposed models outperform the traditional models for the targeted sentiment analysis task. |
Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations (2024.eacl-long)
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| Challenge: | Recent laws like “right to explanations” have spurred research in developing interpretable models . a recent study has shown that multimodal explanations improve performance in generating textual justifications . |
| Approach: | They propose to use visual and textual modalities to explain why a given meme is cyberbullying . they use a Contrastive Language-Image Pretraining approach to generate textual justifications . |
| Outcome: | The proposed model improves performance in visual and textual explanations and identifies the visual evidence supporting a decision. |
Covariance Matrix-Driven Image Channel Allocation for Multimodal Fake News Detection (2026.findings-acl)
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| Challenge: | Existing methods focus on designing efficient multimodal fusion frameworks to bridge the semantic gap between images and texts. |
| Approach: | They propose a covariance matrix-driven image channel allocation method that expands the number of original channel maps and assigns importance scores to the expanded channel maps. |
| Outcome: | The proposed method achieves state-of-the-art on three public multimodal fake news detection benchmark datasets. |
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP (2021.tacl-1)
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| Challenge: | Pretrained language models pick up and reproduce undesirable biases when trained on large, unfiltered crawls from the Internet. |
| Approach: | They propose a decoding algorithm that, given only a textual description of the undesired behavior, reduces the probability of a language model producing problematic text. |
| Outcome: | The proposed approach reduces the probability of a language model producing problematic text by giving only a textual description of the undesired behavior. |
SANDI: Story-and-Images Alignment (2021.eacl-main)
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| Challenge: | a method for selecting images from an image collection and aligning them with text paragraphs of a story is presented . judiciously placed images are used for multimodal descriptions and narration in stories . |
| Approach: | They propose a method for automatically selecting images from an image collection and aligning them with text paragraphs of a story. |
| Outcome: | The proposed method can select and align images with texts with high quality of semantic fit. |
Exploring Paracrawl for Document-level Neural Machine Translation (2023.eacl-main)
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| Challenge: | Document-level neural machine translation (NMT) has outperformed sentence-level NMT on a number of datasets. |
| Approach: | They use Paracrawl to extract parallel paragraphs from Paracral webpages . they also use the extracted parallel paragraph as parallel documents for training . |
| Outcome: | The proposed model outperforms sentence-level NMT on a number of datasets. |
Argument Invention from First Principles (P19-1)
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Yonatan Bilu, Ariel Gera, Daniel Hershcovich, Benjamin Sznajder, Dan Lahav, Guy Moshkowich, Anael Malet, Assaf Gavron, Noam Slonim
| Challenge: | Argument Invention is a task that is often referred to as a natural way of inventing arguments, but has not been formalized in the context of NLP. |
| Approach: | They propose to define a taxonomy of recurring arguments and to automatically identify which of them are relevant to the topic. |
| Outcome: | The proposed taxonomy is coherent, covers the relevant topics and coincides with what debaters actually argue in their speeches, and facilitates automatic argument invention for new topics. |
DISARM: Detecting the Victims Targeted by Harmful Memes (2022.findings-naacl)
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| Challenge: | DISARM is a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and then incorporates a novel contextualized deep neural network to classify whether the meme intends to harm these entities. |
| Approach: | They propose a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and incorporates a novel contextualized deep neural network to classify whether the meme intends to harm them. |
| Outcome: | The proposed framework outperforms 10 unimodal and multimodal systems and reduces error rate of harmful target identification by 9 % absolute over baseline systems. |
A Multimodal Framework to Detect Target Aware Aggression in Memes (2024.eacl-long)
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| Challenge: | Recent research on memes’ detrimental facets is skewed towards high-resource languages, such as Bengali. |
| Approach: | They propose a dataset MIMOSA that annotates annotated memes across five aggression target categories in Bengali and propose 'Multimodal Attentive Fusion' to detect aggression targets. |
| Outcome: | The proposed method outperforms state-of-the-art methods in Bengali and in low-resource languages. |
WAC: A Corpus of Wikipedia Conversations for Online Abuse Detection (2020.lrec-1)
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| Challenge: | Existing methods for moderation of abusive content are limited by the lack of large corpora of conversations. |
| Approach: | They propose a framework with comment-level abuse annotations based on the Wikipedia Comment corpus . they propose 'context-based' approaches to detect abusive content based upon conversational context . |
| Outcome: | The proposed framework can be used to improve the moderation process of abusive content on the Internet. |
BAN-PL: A Polish Dataset of Banned Harmful and Offensive Content from Wykop.pl Web Service (2024.lrec-main)
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Anna Kolos, Inez Okulska, Kinga Głąbińska, Agnieszka Karlinska, Emilia Wisnios, Paweł Ellerik, Andrzej Prałat
| Challenge: | a new dataset of offensive social media content for the Polish language is presented to address this gap . access to accurate and non-synthetic datasets of social media is limited for low-resource languages . |
| Approach: | They present a new open dataset of offensive social media content for the Polish language . authors propose to make the dataset publicly available to improve access . |
| Outcome: | The proposed dataset includes 691,662 posts and comments from the Polish Reddit . the authors describe the dataset and apply it to real-life content moderation processes . |
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
A Joint Learning Framework for Restaurant Survival Prediction and Explanation (2022.emnlp-main)
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| Challenge: | Recent advances in deep learning have various models that research reviews and interactions for different kinds of tasks, such as predicting restaurant survival. |
| Approach: | They propose a joint learning framework for explainable restaurant survival prediction based on multi-modal data of user-restaurant interactions and users’ textual reviews. |
| Outcome: | The proposed framework improves on two datasets showing that it can model restaurant interactions and users’ textual reviews. |
Abusive Language Detection with Graph Convolutional Networks (N19-1)
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| Challenge: | Existing approaches to abusive language detection only capture shallow properties of online communities . a new approach captures both the structure of online community and linguistic behavior of users . |
| Approach: | They propose a graph convolutional network approach that captures the linguistic behavior of users . they propose to model homophily by embeddings for authors that encode the structure of their communities . |
| Outcome: | The proposed approach captures both the structure and linguistic behavior of users in online communities . authors show that the proposed approach significantly advances the current state of the art . |
Aggression-annotated Corpus of Hindi-English Code-mixed Data (L18-1)
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| Challenge: | a number of incidents of aggression and related events have increased over the web . the reach and extent of the Internet has given these events unprecedented power and influence to affect the lives of billions of people. |
| Approach: | They propose to develop an aggression tagset and an annotated corpus of Hindi-English code-mixed data from two of the most popular social networking / social media platforms in India -Twitter and Facebook. |
| Outcome: | The proposed dataset contains approximately 18k tweets and 21k facebook comments and is being released for further research in the field. |
Assessing Multilinguality of Publicly Accessible Websites (2022.lrec-1)
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| Challenge: | multilingualism on the Web is a problem not only at the world level, but also at the European and regional level. |
| Approach: | They propose a tool that automatically analyses the language diversity of the Web and propose indicators and methodologies to measure multilingualism of European websites. |
| Outcome: | The proposed tool can be independently run at set intervals and concludes that multilingualism on the Web is still a problem not only at the world level, but also at the European and regional level. |
CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking (2022.naacl-main)
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| Challenge: | CHEF dataset provides evidence retrieval over non-English claims . e-fact-checking is a time-consuming task, which can take journalists several hours or days. |
| Approach: | They construct a dataset of 10K real-world claims that is based on annotated evidence retrieved from the Internet. |
| Outcome: | The proposed dataset provides evidence retrieval as a latent variable and can be used to train and reason over non-English claims. |
Detecting Harmful Memes and Their Targets (2021.findings-acl)
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Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing body of research on meme analysis has focused on detecting harmful memes and their social entities . a meme is a form of content that is often harmless and designed to look funny . but its multimodal nature and camouflaged semantics make its analysis challenging . |
| Approach: | They propose to use multimodal models to detect harmful memes and identify social entities that harmful meme targets. |
| Outcome: | The proposed model can detect harmful memes and the social entities they target . the proposed model lacks the appropriate contexts and is poorly validated . |
Attending Sentences to detect Satirical Fake News (C18-1)
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| Challenge: | Existing approaches to capture news satire do not explore sentence and document difference . |
| Approach: | They propose a hierarchical deep neural network approach for satire detection . it is able to capture satirical news both at the sentence level and document level . |
| Outcome: | The proposed approach can capture satire at sentence and document levels. |
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability (2021.findings-emnlp)
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| Challenge: | Abuse on the Internet is an important societal problem of our time. |
| Approach: | They propose to use user and community information to enhance detection of abusive language . they propose to propose properties that an explainable method should aim to exhibit . |
| Outcome: | The proposed methods leverage user and community information to enhance detection of abusive language. |
Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval (2024.findings-naacl)
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| Challenge: | Existing question answering systems rely on pre-selected and annotated evidence documents, thus making them inadequate for addressing novel questions. |
| Approach: | They propose to use the common retrieve-then-read QA pipeline and PubMed as a trustworthy collection of medical research documents to answer health questions from three diverse datasets. |
| Outcome: | The proposed approach improves the macro F1 score by 10% by utilizing the common retrieve-then-read QA pipeline and PubMed as a trustworthy collection of medical research documents. |
INVITE: a Testbed of Automatically Generated Invalid Questions to Evaluate Large Language Models for Hallucinations (2023.findings-emnlp)
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| Challenge: | Recent advances in Large language models have enabled them to hold free form conversations over multiple turns, but they exhibit a tendency to make unfounded and incorrect statements, commonly labeled as hallucinations. |
| Approach: | They propose a framework to test large language models for hallucinations using automatically generated INValId questions. |
| Outcome: | The proposed framework is based on a testbed of automatically generated INValId questions to evaluate large language models for hallucinations. |
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets (2021.findings-emnlp)
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Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing number of harmful memes are being used for trolling, cyberbullying and abuse . a new approach to detect harmful meme images and texts is emerging . |
| Approach: | They propose a multimodal deep neural network that detects harmful memes . they extend the recently released HarMeme dataset with additional memes and a new topic . |
| Outcome: | The proposed framework outperforms rival methods in detecting harmful memes and their target social entities. |
Aligning Multidimensional Worldviews and Discovering Ideological Differences (2021.emnlp-main)
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| Challenge: | Existing work on understanding worldviews and ideological distinctions focuses on political polarization . et al., 2018: a novel method for uncovering complex ideological and worldview characteristics of communities. |
| Approach: | They propose a method to uncover multifaceted ideological differences across multiple axes . they use comments from the largest communities on reddit.com to train word embedding models . |
| Outcome: | The proposed method can uncover complex ideological differences across multiple axes of polarization using over 1B comments from the largest communities on reddit.com representing 40% of Reddit activity. |
Deciphering Hate: Identifying Hateful Memes and Their Targets (2024.acl-long)
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| Challenge: | a growing body of research has focused on the negative aspects of memes in high-resource languages like Bengali . a new dataset for Bengali hateful memes is designed to detect their targeted entities . |
| Approach: | They propose a multimodal dataset that analyzes the modality of memes and compares them with other datasets. |
| Outcome: | The proposed dataset outperforms state-of-the-art datasets on Bengali hateful memes . the proposed dataset is generalizable on other low-resource hateful memes datasets compared with baselines based on the proposed model . |
ReSee: Responding through Seeing Fine-grained Visual Knowledge in Open-domain Dialogue (2023.emnlp-main)
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| Challenge: | Existing multimodal dialogue systems are limited by the scale and quality of available datasets or the coarse concept of visual knowledge. |
| Approach: | They propose to explicitly split visual knowledge into finer granularity and turn-level . they propose a framework to add visual representation into vanilla dialogue models . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on automatic and human evaluations. |
Pre-training CLIP against Data Poisoning with Optimal Transport-based Matching and Alignment (2025.emnlp-main)
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| Challenge: | Recent studies have shown that Contrastive Language-Image Pre-training (CLIP) models are vulnerable to data poisoning and backdoor attacks due to massive training image-caption pairs crawled from the Internet. |
| Approach: | They propose an Optimal Transport-based framework to reconstruct image-caption pairs and propose an optimal transport-based distance measure to re-assign new captions based on the proposed optimal transport distance. |
| Outcome: | The proposed framework reduces the attack success rates of poisoning attacks to 0% in most cases. |
Exploring the Capability of Multimodal LLMs with Yonkoma Manga: The YManga Dataset and Its Challenging Tasks (2024.findings-emnlp)
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| Challenge: | YManga dataset is the first specifically designed for yonkoma manga understanding . |
| Approach: | They propose to use a dataset of 1,015 yonkoma strips with 10,150 human annotations to define three tasks for panel sequence detection, intent generation and description generation for masked panels. |
| Outcome: | The proposed dataset contains 1,015 high-quality yonkoma strips with 10,150 human annotations. |
Detecting Propaganda Techniques in Memes (2021.acl-long)
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Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino
| Challenge: | Propaganda can be defined as a form of communication that aims to influence opinions or the actions of people towards a specific goal. |
| Approach: | They propose to detect the type of propaganda techniques used in memes by annotating them with 22 techniques. |
| Outcome: | The proposed model identifies 22 propaganda techniques in memes, which can appear in text, image or both . |
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents (2020.coling-main)
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| Challenge: | Existing studies suggest that Neural Machine Translation still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. |
| Approach: | They propose to evaluate the robustness of Neural Machine Translation models against specific linguistic phenomena in Japanese-English translation. |
| Outcome: | The proposed model can handle user-generated content (UGC) on the Internet, but it is difficult to translate clean inputs. |
Mama/Papa, Is this Text for Me? (2020.coling-main)
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Rashedur Rahman, Gwénolé Lecorvé, Aline Étienne, Delphine Battistelli, Nicolas Béchet, Jonathan Chevelu
| Challenge: | Existing methods to predict minimal age from which text can be understood for children are unresolved in computational linguistics. |
| Approach: | They propose a method which predicts the minimum age from which a text can be understood by a recurrent neural network. |
| Outcome: | The proposed method outperforms state-of-the-art models at sentence and text levels and achieves mean absolute errors of 1.86 and 2.28. |
Shami: A Corpus of Levantine Arabic Dialects (L18-1)
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| Challenge: | Modern Standard Arabic is the official written language used in education and media . however, the spoken language varies widely across the Arab world . |
| Approach: | They construct a levantine dialect corpus covering data from four dialects spoken in four countries . they describe rules for pre-processing without affecting the meaning so that it is processable by NLP tools. |
| Outcome: | The proposed corpus is larger than existing corpora in terms of size, words and vocabularies. |
Annotating Perspectives on Vaccination (2020.lrec-1)
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| Challenge: | Vaccination corpus is a corpus of texts related to the online vaccination debate . it contains documents from the Internet which reflect different views on vaccinations . |
| Approach: | They present a corpus of texts related to the online vaccination debate annotated with perspectives about attribution, claims and opinions. |
| Outcome: | The Vaccination Corpus contains 294 documents from the Internet which reflect different views on vaccinations. |
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking (2024.lrec-main)
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| Challenge: | determining the trustworthiness of online medical content is challenging in the digital age . fact-checking is an approach to assess the veracity of factual claims . a new dataset is presented to help advance automated fact- checking . |
| Approach: | They propose a dataset that assesses the veracity of factual claims using evidence from credible sources. |
| Outcome: | The proposed dataset can be used for automated fact-checking tasks. |
Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve? (2024.emnlp-main)
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| Challenge: | In the last decade, the generalization and adaptation abilities of deep learning models were evaluated on fixed training and test distributions. |
| Approach: | They propose to train large language models on unlabeled text corpora and train them online. |
| Outcome: | The proposed model training on a text domain could degrade its perplexity on the test portion of the same domain. |