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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 13 of 13 1 TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment ... Yang, Jianwei; Bisk, Yonatan; Gao, Jianfeng. - : arXiv, 2021 BASE Show details 2 Few-shot Language Coordination by Modeling Theory of Mind ... Zhu, Hao; Neubig, Graham; Bisk, Yonatan. - : arXiv, 2021 BASE Show details 3 An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games ... Suglia, Alessandro; Bisk, Yonatan; Konstas, Ioannis. - : arXiv, 2021 BASE Show details 4 Grounding 'Grounding' in NLP ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Alan; Bisk, Yonatan. - : Underline Science Inc., 2021 BASE Show details 5 Dependency Induction Through the Lens of Visual Perception ... Su, Ruisi; Rijhwani, Shruti; Zhu, Hao. - : arXiv, 2021 BASE Show details 6 Dependency Induction Through the Lens of Visual Perception ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Hao; Bisk, Yonatan; He, Junxian; Neubig, Graham; Rijhwani, Shruti; Su, Ruisi; Wang, Xinyu. - : Underline Science Inc., 2021 Abstract: Most previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recently introduced visually grounded syntax models make use of multimodal information leading to improved performance in constituency grammar induction. However, as compared to dependency grammars, constituency grammars do not provide a straight- forward way to incorporate visual information without enforcing language-specific heuristics. In this paper, we propose an unsupervised grammar induction model that leverages word concreteness and a structural vision-based heuristic to jointly learn constituency-structure and dependency-structure grammars. Our experiments find that concreteness is a strong indicator for learning dependency grammars, im- proving the direct attachment score (DAS) by over 50% as compared to state-of-the-art models trained on pure text. Next, we propose an extension of our model that leverages both word concreteness ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://dx.doi.org/10.48448/7j5q-9w68 https://underline.io/lecture/39855-dependency-induction-through-the-lens-of-visual-perception BASE Hide details 7 Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games ... Suglia, Alessandro; Vergari, Antonio; Konstas, Ioannis. - : arXiv, 2020 BASE Show details 8 Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games ... The 28th International Conference on Computational Linguistics 2020; Bastianelli, Emanuele; Bisk, Yonatan. - : Underline Science Inc., 2020 BASE Show details 9 Experience Grounds Language ... Bisk, Yonatan; Holtzman, Ari; Thomason, Jesse. - : arXiv, 2020 BASE Show details 10 The Return of Lexical Dependencies: Neural Lexicalized PCFGs ... Zhu, Hao; Bisk, Yonatan; Neubig, Graham. - : arXiv, 2020 BASE Show details 11 Shifting the Baseline: Single Modality Performance on Visual Navigation & QA ... Thomason, Jesse; Gordon, Daniel; Bisk, Yonatan. - : arXiv, 2018 BASE Show details 12 Natural Language Inference from Multiple Premises ... Lai, Alice; Bisk, Yonatan; Hockenmaier, Julia. - : arXiv, 2017 BASE Show details 13 Unsupervised grammar induction with Combinatory Categorial Grammars Bisk, Yonatan Yitzhak. - 2015 BASE Show details

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