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1
Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
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2
End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ...
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3
To what extent do human explanations of model behavior align with actual model behavior? ...
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4
Elementary-Level Math Word Problem Generation using Pre-Trained Transformers ...
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5
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations ...
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6
EM ALBERT: a step towards equipping Manipuri for NLP ...
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7
Language, Brains & Interpretability ...
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8
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision ...
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9
Coral: An Approach for Conversational Agents in Mental Health Applications ...
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10
#WhyDidTheyStay: An NLP-driven approach to analyzing the factors that affect domestic violence victims ...
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11
SciBERT-based Multitasking Deep Neural Architecture to identify Contribution Statements from Scientific articles ...
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12
"I don't know who she is": Discourse and Knowledge Driven Coreference Resolution ...
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13
Certified Robustness to Programmable Transformations in LSTMs ...
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14
Sinhala-English Code-mixed and Code-switched Data Classification ...
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15
Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution ...
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16
Adverse Drug Reaction Classification of Tweets with Fusion of Text and Drug Representations ...
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17
Learning Cross-lingual Representations for Event Coreference Resolution with Multi-view Alignment and Optimal Transport ...
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18
VisualSem: a high-quality knowledge graph for vision and language ...
Abstract: An exciting frontier in natural language understanding (NLU) and generation (NLG) calls for (vision-and-) language models that can efficiently access external structured knowledge repositories. However, many existing knowledge bases only cover limited domains, or suffer from noisy data, and most of all are typically hard to integrate into neural language pipelines. To fill this gap, we release VisualSem: a high-quality knowledge graph (KG) which includes nodes with multilingual glosses, multiple illustrative images, and visually relevant relations. We also release a neural multi-modal retrieval model that can use images or sentences as inputs and retrieves entities in the KG. This multi-modal retrieval model can be integrated into any (neural) model pipeline. We encourage the research community to use VisualSem for data augmentation and/or as a source of grounding, among other possible uses. VisualSem as well as the multi-modal retrieval models are publicly available and can be downloaded in this URL: ...
Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural language generation; Natural Language Processing; Natural Language Understanding; Neural Network
URL: https://dx.doi.org/10.48448/bqpj-ty92
https://underline.io/lecture/39634-visualsem-a-high-quality-knowledge-graph-for-vision-and-language
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19
Specializing Multilingual Language Models: An Empirical Study ...
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20
On the Language-specificity of Multilingual BERT and the Impact of Fine-tuning ...
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