PluRule is a new multimodal multilingual benchmark showing that state-of-the-art vision-language models perform only marginally better than a trivial baseline at detecting specific rule violations in pluralistic online communities.
Computational Linguistics , volume =
4 Pith papers cite this work. Polarity classification is still indexing.
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GiLT augments Transformers with semantic dependency graphs by modulating attention to improve syntactic generalization while keeping perplexity competitive and enabling better finetuning on downstream tasks.
Proposes treating Pāṇini's Astādhyāyī as a unifying computational architecture and benchmark foundation for Indic language NLP to improve accuracy, data efficiency, and transfer.
A symbolic system extracts events from 450 property crime reports, with 54.1% high-confidence outputs, 93.7% mapped via PropBank-VerbNet-WordNet, and 100% human agreement on incident initiation, stolen items, and temporal cues.
citing papers explorer
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PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media
PluRule is a new multimodal multilingual benchmark showing that state-of-the-art vision-language models perform only marginally better than a trivial baseline at detecting specific rule violations in pluralistic online communities.
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GiLT: Augmenting Transformer Language Models with Dependency Graphs
GiLT augments Transformers with semantic dependency graphs by modulating attention to improve syntactic generalization while keeping perplexity competitive and enabling better finetuning on downstream tasks.
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A P\={a}ninian Foundation for Indic Language Processing
Proposes treating Pāṇini's Astādhyāyī as a unifying computational architecture and benchmark foundation for Indic language NLP to improve accuracy, data efficiency, and transfer.
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Ontology for Policing: Conceptual Knowledge Learning for Semantic Understanding and Reasoning in Law Enforcement Reports
A symbolic system extracts events from 450 property crime reports, with 54.1% high-confidence outputs, 93.7% mapped via PropBank-VerbNet-WordNet, and 100% human agreement on incident initiation, stolen items, and temporal cues.