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EnvEdit: Environment Editing for Vision-and-Language Navigation ...
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Homepage2Vec: Language-Agnostic Website Embedding and Classification ...
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Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER ...
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A new approach to calculating BERTScore for automatic assessment of translation quality ...
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A New Generation of Perspective API: Efficient Multilingual Character-level Transformers ...
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ViWOZ: A Multi-Domain Task-Oriented Dialogue Systems Dataset For Low-resource Language ...
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EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation ...
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Learning Bidirectional Translation between Descriptions and Actions with Small Paired Data ...
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A Feasibility Study of Answer-Agnostic Question Generation for Education ...
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Language Generation for Broad-Coverage, Explainable Cognitive Systems ...
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Cross-view Brain Decoding ...
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Abstract:
How the brain captures the meaning of linguistic stimuli across multiple views is still a critical open question in neuroscience. Consider three different views of the concept apartment: (1) picture (WP) presented with the target word label, (2) sentence (S) using the target word, and (3) word cloud (WC) containing the target word along with other semantically related words. Unlike previous efforts, which focus only on single view analysis, in this paper, we study the effectiveness of brain decoding in a zero-shot cross-view learning setup. Further, we propose brain decoding in the novel context of cross-view-translation tasks like image captioning (IC), image tagging (IT), keyword extraction (KE), and sentence formation (SF). Using extensive experiments, we demonstrate that cross-view zero-shot brain decoding is practical leading to ~0.68 average pairwise accuracy across view pairs. Also, the decoded representations are sufficiently detailed to enable high accuracy for cross-view-translation tasks with ... : 11 pages, 10 figures ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Biological sciences; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Image and Video Processing eess.IV; Machine Learning cs.LG; Neurons and Cognition q-bio.NC
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URL: https://dx.doi.org/10.48550/arxiv.2204.09564 https://arxiv.org/abs/2204.09564
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Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency ...
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Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics ...
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Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity ...
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Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding ...
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