Social media plays a pivotal role in global discussions, including on climate change. The variety of opinions expressed range from supportive to oppositional, with some instances of hate speech. Recognizing the importance of understanding these varied perspectives, the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) at EACL 2024 hosted a shared task focused on detecting stances and hate speech in climate activism-related tweets. This task was divided into three subtasks: subtasks A and B concentrated on identifying hate speech and its targets, while subtask C focused on stance detection. Participants’ performance was evaluated using the macro F1-score. With over 100 teams participating, the highest F1 scores achieved were 91.44% in subtask C, 78.58% in subtask B, and 74.83% in subtask A. This paper details the methodologies of 24 teams that submitted their results to the competition’s leaderboard.

Science communication, in layperson’s terms, is essential to reach the general population and also maximize the impact of underlying scientific research. Hence, good science blogs and journalistic reviews of research articles are so well-read and critical to conveying science. Scientific blogging goes beyond traditional research summaries, offering experts a platform to articulate findings in layperson’s terms. It bridges the gap between intricate research and its comprehension by the general public, policymakers, and other researchers. Amid the rapid expansion of scientific data and the accelerating pace of research, credible science blogs serve as vital artifacts for evidence-based information to the general non-expert audience. However, writing a scientific blog or even a short lay summary requires significant time and effort. Here, we are intrigued what if the process of writing a scientific blog based on a given paper could be semi-automated to produce the first draft? In this paper, we introduce a novel task of Artificial Intelligence (AI)-based science blog generation from a research article. We leverage the idea that presentations and science blogs share a symbiotic relationship in their aim to clarify and elucidate complex scientific concepts. Both rely on visuals, such as figures, to aid comprehension. With this motivation, we create a new dataset of science blogs using the presentation transcript and the corresponding slides. We create a dataset containing a paper’s presentation transcript and figures annotated from nearly 3000 papers. We then propose a multimodal attention model to generate a blog text and select the most relevant figures to explain a research article in layperson’s terms, essentially a science blog. Our experimental results with respect to both automatic and human evaluation metrics show the effectiveness of our proposed approach and the usefulness of our proposed dataset.

We describe SemEval-2023 Task 11 on behavioral segregation of annotations to find the similarities and contextual thinking of a group of annotators. We have utilized a behavioral segmentation analysis on the annotators to model them independently and combine the results to yield soft and hard scores. Our team focused on experimenting with hierarchical clustering with various distance metrics for similarity, dissimilarity, and reliability. We modeled the clusters and assigned weightage to find the soft and hard scores. Our team was able to find out hidden behavioral patterns among the judgments of annotators after rigorous experiments. The proposed system is made available.

This paper presents “HaVQA”, the first multimodal dataset for visual question answering (VQA) tasks in the Hausa language. The dataset was created by manually translating 6,022 English question-answer pairs, which are associated with 1,555 unique images from the Visual Genome dataset. As a result, the dataset provides 12,044 gold standard English-Hausa parallel sentences that were translated in a fashion that guarantees their semantic match with the corresponding visual information. We conducted several baseline experiments on the dataset, including visual question answering, visual question elicitation, text-only and multimodal machine translation.

This paper offers an in-depth overview of the team “ODIAGEN’s” translation system submitted to the Workshop on Asian Translation (WAT2023). Our focus lies in the domain of Indic Multimodal tasks, specifically targeting English to Hindi, English to Malayalam, and English to Bengali translations. The system uses a state-of-the-art Transformer-based architecture, specifically the NLLB-200 model, fine-tuned with language-specific Visual Genome Datasets. With this robust system, we were able to manage both text-to-text and multimodal translations, demonstrating versatility in handling different translation modes. Our results showcase strong performance across the board, with particularly promising results in the Hindi and Bengali translation tasks. A noteworthy achievement of our system lies in its stellar performance across all text-to-text translation tasks. In the categories of English to Hindi, English to Bengali, and English to Malayalam translations, our system claimed the top positions for both the evaluation and challenge sets. This system not only advances our understanding of the challenges and nuances of Indic language translation but also opens avenues for future research to enhance translation accuracy and performance.

This paper introduces the proposed summarization system of the AINLPML team for the First Shared Task on Multi-Perspective Scientific Document Summarization at SDP 2022. We present a method to produce abstractive summaries of scientific documents. First, we perform an extractive summarization step to identify the essential part of the paper. The extraction step includes utilizing a contributing sentence identification model to determine the contributing sentences in selected sections and portions of the text. In the next step, the extracted relevant information is used to condition the transformer language model to generate an abstractive summary. In particular, we fine-tuned the pre-trained BART model on the extracted summary from the previous step. Our proposed model successfully outperformed the baseline provided by the organizers by a significant margin. Our approach achieves the best average Rouge F1 Score, Rouge-2 F1 Score, and Rouge-L F1 Score among all submissions.