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Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality ...
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ANLIzing the Adversarial Natural Language Inference Dataset
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection ...
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FLAVA: A Foundational Language And Vision Alignment Model ...
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I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling ...
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Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation ...
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Gradient-based Adversarial Attacks against Text Transformers ...
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On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study ...
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Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little ...
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Deep Artificial Neural Networks Reveal a Distributed Cortical Network Encoding Propositional Sentence-Level Meaning
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In: J Neurosci (2021)
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Emergent Linguistic Phenomena in Multi-Agent Communication Games ...
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Inferring concept hierarchies from text corpora via hyperbolic embeddings ...
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Inferring concept hierarchies from text corpora via hyperbolic embeddings
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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) (2019)
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Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns ...
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Abstract:
Important advances have recently been made using computational semantic models to decode brain activity patterns associated with concepts; however, this work has almost exclusively focused on concrete nouns. How well these models extend to decoding abstract nouns is largely unknown. We address this question by applying state-of-the-art computational models to decode functional Magnetic Resonance Imaging (fMRI) activity patterns, elicited by participants reading and imagining a diverse set of both concrete and abstract nouns. One of the models we use is linguistic, exploiting the recent word2vec skipgram approach trained on Wikipedia. The second is visually grounded, using deep convolutional neural networks trained on Google Images. Dual coding theory considers concrete concepts to be encoded in the brain both linguistically and visually, and abstract concepts only linguistically. Splitting the fMRI data according to human concreteness ratings, we indeed observe that both models significantly decode the most ... : Stephen Clark is supported by ERC Starting Grant DisCoTex (306920). ...
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URL: https://www.repository.cam.ac.uk/handle/1810/263633 https://dx.doi.org/10.17863/cam.8991
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Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research ...
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HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment ...
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