Ireland

Hope Speech detection is the task of classifying a sentence as hope speech or non-hope speech given a corpus of sentences. Hope speech is any message or content that is positive, encouraging, reassuring, inclusive, and supportive that in- spires and engenders optimism in the minds of people. In contrast to identifying and censoring
negative speech patterns, hope speech detection focused on recognizing and promoting positive speech patterns online. In this paper, we report an overview of the findings and results from the shared task on hope speech detection for Tamil, Malayalam, Kannada, English, and Spanish languages conducted at the second workshop on Language Technology for Equality, Diversity, and Inclusion (LT-EDI-2022), organized as a part of ACL 2022. The participants were provided with annotated training &amp; development datasets and unlabelled test datasets in all five
languages. The goal of the shared task is to classify the given sentences into one of the two hope speech classes (Hope speech, Non-hope speech). A total of 126 participants registered for the shared task and 14 teams finally submitted their results. The performance of the systems submitted was evaluated in terms of
micro-F1 score and weighted-F1 score. The datasets for this challenge are openly available on the competition website.

ACL 2022

Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion

hope speech

under-resourced languages

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

Hope Speech detection is the task of classifying a sentence as hope speech or non-hope speech given a corpus of sentences. Hope speech is any message or content that is positive, encouraging, reassuring, inclusive, and supportive that in- spires and engenders optimism in the minds of people. In contrast to identifying and censoring
negative speech patterns, hope speech detection focused on recognizing and promoting positive speech patterns online. In this paper, we report an overview of the findings and results from the shared task on hope speech detection for Tamil, Malayalam, Kannada, English, and Spanish languages conducted at the second workshop on Language Technology for Equality, Diversity, and Inclusion (LT-EDI-2022), organized as a part of ACL 2022. The participants were provided with annotated training & development datasets and unlabelled test datasets in all five
languages. The goal of the shared task is to classify the given sentences into one of the two hope speech classes (Hope speech, Non-hope speech). A total of 126 participants registered for the shared task and 14 teams finally submitted their results. The performance of the systems submitted was evaluated in terms of
micro-F1 score and weighted-F1 score. The datasets for this challenge are openly available on the competition website.

workshop paper

Recently, detection and categorization of undesired (e. g., aggressive, abusive, offensive, hate) content from online platforms has grabbed the attention of researchers because of its detrimental impact on society. Several attempts have been made to mitigate the usage and propagation of such content. However, most past studies were conducted primarily for English, where low-resource languages like Bengali remained out of the focus. Therefore, to facilitate research in this arena, this paper introduces a novel multilabel Bengali dataset (named M-BAD) containing 15650 texts to detect aggressive texts and their targets. Each text of M-BAD went through rigorous two-level annotations. At the primary level, each text is labelled as either aggressive or non-aggressive. In the secondary level, the aggressive texts have been further annotated into five fine-grained target classes: religion, politics, verbal, gender and race. Baseline experiments are carried out with different machine learning (ML), deep learning (DL) and transformer models, where Bangla-BERT acquired the highest weighted f_1-score in both detection (0.92) and target identification (0.83) tasks. Error analysis of the models exhibits the difficulty to identify context-dependent aggression, and this work argues that further research is required to address these issues.

M-BAD: A Multilabel Dataset for Detecting Aggressive Texts and Their Targets

This study investigates how fake news uses a thumbnail for a news article with a focus on whether a news article’s thumbnail represents the news content correctly. A news article shared with an irrelevant thumbnail can mislead readers into having a wrong impression of the issue, especially in social media environments where users are less likely to click the link and consume the entire content. We propose to capture the degree of semantic incongruity in the multimodal relation by using the pretrained CLIP representation. From a source-level analysis, we found that fake
news employs a more incongruous image to the main content than general news. Going further, we attempted to detect news articles with image-text incongruity. Evaluation experiments suggest that CLIP-based methods can successfully detect news articles in which the thumbnail is semantically irrelevant to news text. This study contributes to the research by providing a novel view on tackling online fake
news and misinformation. Code and datasets are available at https://github.com/s
su-humane/fake-news-thumbnail.

How does fake news use a thumbnail? CLIP-based Multimodal Detection on the Unrepresentative News Image

The COVID-19 pandemic has created severe threats to global health control. In particular, misinformation circulated on social media and news outlets has undermined public trust in government and health agencies. This problem is further exacerbated in developing countries or low-resource regions where the
news may not be equipped with abundant English fact-checking information. This poses a question: “are existing computational solutions toward misinformation also effective in low-resource regions?" In this paper, to answer this question, we make the first attempt to detect COVID-19 misinformation in English, Spanish,
and Haitian French populated in the Caribbean region, using the fact-checked claims in US-English. We started by collecting a dataset of real & false claims in the Caribbean region. Then we trained several classification and language models on COVID-19 from high-resource language regions and transferred this knowledge to the Caribbean claim dataset. The experimental results show the limitations of
current false claim detection in low-resource regions and encourage further research toward the detection of multi-lingual false claims in long tail.

Detecting False Claims in Low-Resource Regions: A Case Study of Caribbean Islands

Presents two proposed frameworks to role label the entities present in a meme in hero,villain,victim semantic classes using NLP techiques like NER and Sentiment Analysis. Here our aim is that given a meme from the context we sholud be able to tell whether an entity either hero or villain or victim or none.

Are you a hero or a villain? A semantic role labelling approach for detecting harmful memes.

This paper describes our system for the Constraint 2022 challenge at ACL 2022, whose goal is to detect which entities are glorified, vilified or victimized, within a meme. The task should be done considering the perspective of the meme’s author. In our work, the challenge is treated as a multi-class classification task. For a given pair of a meme and an entity, we need to classify whether the entity is being referenced as Hero, a Villain, a Victim or an Other. Our solution combines (ensembling) different models based on the Unimodal(Text only) model and the Multimodal model (Text+ Images). We conduct several experiments and benchmark different competitive pre-trained transformers and vision models in this work. Our solution, based on an ensembling method, is ranked first on the leaderboard and obtains a macro F1 score of 0.58 on the test set.

Logically at the Constraint 2022: Multimodal role labelling

This paper describes the system we developed for the shared task ``Hero, Villain and Victim: Dissecting harmful memes for Semantic role labeling of entities'' organized in the framework of the Second Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situation (Constraint 2022). 
We present an ensemble approach combining transformer-based models and linguistic information, such as the presence of irony and implicit sentiment associated to the target named entities. The ensemble system obtains promising classification scores, with a macro F-score of 0.55, resulting in a third place finish in the competition.

Combining Language Models and Linguistic Information to Label Entities in Memes

Harmful or abusive online content has been increasing over time and it has been raising concerns among social media platforms, government agencies, and policymakers. Such harmful or abusive content can have significant negative impact on society, e.g., cyberbullying can lead to suicides, rumors about COVID-19 can cause vaccine hesitance, promotion of fake cures for COVID-19 can cause health harms and deaths. The content that is posted and shared online can be textual, visual, or a combination of both, e.g., in a meme. Here, we describe our experiments in detecting the roles of the entities (hero, villain, victim) in harmful memes, which is part of the CONSTRAINT-2022 shared task, as well as our system for the task. We further provide a comparative analysis on different experimental settings (i.e., unimodal, multimodal, attention, and augmentation). For reproducibility, we make the experimental code publicly available (https://github.com/robi56/harmful_memes_block_fusion)

Detecting the Role of an Entity in Harmful Memes: Techniques and their Limitations

We propose our solution to the multimodal semantic role labeling task from the CONSTRAINT'22 workshop. The task aims at classifying entities in memes into classes such as ``hero'' and ``villain''. We use several pre-trained multi-modal models to jointly encode the text and image of the memes, and implement three systems to classify the role of the entities. We propose dynamic sampling strategies to tackle the issue of class imbalance. Finally, we perform a qualitative analysis of the representations of the entities.


Fine-tuning and Sampling Strategies for Multimodal Role Labeling of Entities under Class Imbalance

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. The task aims to delve deeper into the domain of meme comprehension by deciphering the connotations behind the entities present in a meme. In more nuanced terms, the shared task focuses on determining the victimizing, glorifying, and vilifying intentions embedded in meme entities to explicate their connotations. To this end, we curate HVVMemes, a novel meme dataset of about 7000 memes spanning the domains of COVID-19 and US Politics, each containing entities and their associated roles: hero, villain, victim, or none. The shared task attracted 105 participants, but eventually only 6 submissions were made. The successful submissions majorly relied on an ensemble of language and multimodal models. The best submission achieved an F1-score of 58.67.

Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes

the COVID-19 pandemic, the spread of misinformation on online social media has grown exponentially. Unverified bogus claims on these platforms regularly mislead people, leading them to believe in half-baked truths. The current vogue is to employ manual fact-checkers to verify claims to combat this avalanche of misinformation. However, establishing such claims' veracity is becoming increasingly challenging, partly due to the plethora of information available, which is difficult to process manually. Thus, it becomes imperative to verify claims automatically without human interventions. To cope up with this issue, we propose an automated claim verification solution encompassing two steps -- document retrieval and veracity prediction. For the retrieval module, we employ a hybrid search-based system with BM25 as a base retriever and experiment with recent state-of-the-art transformer-based models for re-ranking. Furthermore, we use a BART-based textual entailment architecture to authenticate the retrieved documents in the later step. We report experimental findings, demonstrating that our retrieval module outperforms the best baseline system by 10.32 NDCG@100 points. We escort a demonstration to assess the efficacy and impact of our suggested solution. As a byproduct of this study, we present an open-source, easily deployable, and user-friendly Python API that the community can adopt. 


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M-BAD: A Multilabel Dataset for Detecting Aggressive Texts and Their Targets

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Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM

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Next from ACL 2022

M-BAD: A Multilabel Dataset for Detecting Aggressive Texts and Their Targets

Similar lecture

Hope Speech Detection using Data Re-Sampling and 1D Conv-LSTM

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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