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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. - : Underline Science Inc., 2021 BASE Show 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 Abstract: Language is a highly structured medium for communication. An idea starts in the speaker's mind (semantics) and is transformed into a well formed, intelligible, sentence via the specific syntactic rules of a language. We aim to discover the fingerprints of this process in the choice and location of words used in the final utterance. What is unclear is how much of this latent process can be discovered from the linguistic signal alone and how much requires shared non-linguistic context, knowledge, or cues. Unsupervised grammar induction is the task of analyzing strings in a language to discover the latent syntactic structure of the language without access to labeled training data. Successes in unsupervised grammar induction shed light on the amount of syntactic structure that is discoverable from raw or part-of-speech tagged text. In this thesis, we present a state-of-the-art grammar induction system based on Combinatory Categorial Grammars. Our choice of syntactic formalism enables the first labeled evaluation of an unsupervised system. This allows us to perform an in-depth analysis of the system’s linguistic strengths and weaknesses. In order to completely eliminate reliance on any supervised systems, we also examine how performance is affected when we use induced word clusters instead of gold-standard POS tags. Finally, we perform a semantic evaluation of induced grammars, providing unique insights into future directions for unsupervised grammar induction systems. Keyword: Combinatory Categorial Grammar (CCG); Grammar Induction; Unsupervised Methods URL: http://hdl.handle.net/2142/89027 BASE Hide details

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