The proceedings contain 8 papers. The topics discussed include: efficient two-stage progressive quantization of BERT;KGRefiner: knowledge graph refinement for improving accuracy of translational link prediction method...
ISBN:
(纸本)9781959429241
The proceedings contain 8 papers. The topics discussed include: efficient two-stage progressive quantization of BERT;KGRefiner: knowledge graph refinement for improving accuracy of translational link prediction methods;algorithmic diversity and tiny models: comparing binary networks and the fruit fly algorithm on document representation tasks;look ma, only 400 samples! revisiting the effectiveness of automatic N-Gram rule generation for spelling normalization in Filipino;who says elephants can’t run: bringing large scale MoE models into cloud scale production;data-efficient auto-regressive document retrieval for fact verification;AfroLM: a self-active learning-based multilingual pretrained language model for 23 African languages;and towards fair dataset distillation for text classification.
Multiple-choice questions (MCQs) are commonly used in educational assessments and professional certification examinations. However, managing vast collections of MCQs presents numerous challenges, including maintaining...
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Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users’ understanding (Slack et al., 2023;Shen et al., 2023), as one-off explanations may fall sho...
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Extraction of supportive premises for a mathematical problem can contribute to profound success in improving automatic reasoning systems. One bottleneck in automated theorem proving is the lack of a proper semantic in...
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Adversarial attack research in naturallanguageprocessing (NLP) has made significant progress in designing powerful attack methods and defence approaches. However, few efforts have sought to identify which source sam...
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This paper studies the problem of injecting factual knowledge into large pre-trained language models. We train adapter modules on parts of the ConceptNet knowledge graph using the masked language modeling objective an...
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Most domain-specific BERT models are designed to work with short sentences and do not deal with the limitation of 512 tokens in the default BERT tokenizer. This limitation is further exacerbated if the tokenizer has h...
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This article provides a thorough mapping of NLP and language Technology research on 39 European languages onto 46 domains. Our analysis is based on almost 50,000 papers published between 2010 and October 2022 in the A...
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Automated theorem proving can benefit a lot from methods employed in naturallanguageprocessing, knowledge graphs and information retrieval: this non-trivial task combines formal languages understanding, reasoning, s...
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This workshop explores the intersection of media trust, journalistic authority and the role of Artificial Intelligence (AI) within platform-based media ecosystems. As social media becomes a primary source of news for ...
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ISBN:
(纸本)9798400713293
This workshop explores the intersection of media trust, journalistic authority and the role of Artificial Intelligence (AI) within platform-based media ecosystems. As social media becomes a primary source of news for global audiences media uses are changing public discourses and trust in journalism. The workshop includes papers that use innovative methods to analyze media trust and intertwining role of platforms (e.g. Facebook, Instagram, TikTok, and X). Key topics include AI-powered news curation, personalized news feeds and the impact of algorithm-driven content. Advanced techniques such as naturallanguageprocessing, sentiment analysis and semantically enriched entity models are central to understanding and visualizing interactions between users, news providers and content. By integrating these approaches, the workshop aims to foster interdisciplinary discussions, propose new analytical frameworks and contribute to the development of balanced, transparent and fair news ecosystems in the digital age.
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