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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 Page: 1 2 3 Hits 1 – 20 of 48 1 Trajectory Prediction with Linguistic Representations Kuo, Yen-Ling; Huang, Xin; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), International Conference on Robotics and Automation (ICRA), 2022 BASE Show details 2 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 5 Compositional Networks Enable Systematic Generalization for Grounded Language Understanding Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021 BASE Show details 6 Trajectory Prediction with Linguistic Representations ... Kuo, Yen-Ling; Huang, Xin; Barbu, Andrei. - : arXiv, 2021 BASE Show details 7 Measuring Social Biases in Grounded Vision and Language Embeddings ... NAACL 2021 2021; Barbu, Andrei; Katz, Boris. - : Underline Science Inc., 2021 BASE Show details 8 Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Barbu, Andrei; Katz, Boris. - : Underline Science Inc., 2021 BASE Show details 9 Assessing Language Proficiency from Eye Movements in Reading Berzak, Yevgeni; Katz, Boris; Levy, Roger P In: Association for Computational Linguistics (2021) BASE Show details 10 Measuring Social Biases in Grounded Vision and Language Embeddings Ross, Candace; Barbu, Andrei; Katz, Boris. - : Center for Brains, Minds and Machines (CBMM), Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), 2021 BASE Show details 11 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 12 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 13 Learning a natural-language to LTL executable semantic parser for grounded robotics Wang, Christopher; Ross, Candace; Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), Conference on Robot Learning (CoRL), 2020 Abstract: Children acquire their native language with apparent ease by observing how language is used in context and attempting to use it themselves. They do so without laborious annotations, negative examples, or even direct corrections. We take a step toward robots that can do the same by training a grounded semantic parser, which discovers latent linguistic representations that can be used for the execution of natural-language commands. In particular, we focus on the difficult domain of commands with a temporal aspect, whose semantics we capture with Linear Temporal Logic, LTL. Our parser is trained with pairs of sentences and executions as well as an executor. At training time, the parser hypothesizes a meaning representation for the input as a formula in LTL. Three competing pressures allow the parser to discover meaning from language. First, any hypothesized meaning for a sentence must be permissive enough to reflect all the annotated execution trajectories. Second, the executor — a pretrained end-to-end LTL planner — must find that the observed trajectories are likely executions of the meaning. Finally, a generator, which reconstructs the original input, encourages the model to find representations that conserve knowledge about the command. Together these ensure that the meaning is neither too general nor too specific. Our model generalizes well, being able to parse and execute both machine-generated and human-generated commands, with near-equal accuracy, despite the fact that the human-generated sentences are much more varied and complex with an open lexicon. The approach presented here is not specific to LTL: it can be applied to any domain where sentence meanings can be hypothesized and an executor can verify these meanings, thus opening the door to many applications for robotic agents. ; This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. Keyword: LTL; semantic parsing; weak supervision URL: https://hdl.handle.net/1721.1/141340 BASE Hide details 14 Compositional Networks Enable Systematic Generalization for Grounded Language Understanding ... Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : arXiv, 2020 BASE Show details 15 Learning a natural-language to LTL executable semantic parser for grounded robotics ... Wang, Christopher; Ross, Candace; Kuo, Yen-Ling. - : arXiv, 2020 BASE Show details 16 Measuring Social Biases in Grounded Vision and Language Embeddings ... Ross, Candace; Katz, Boris; Barbu, Andrei. - : arXiv, 2020 BASE Show details 17 Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas Kuo, Yen-Ling; Katz, Boris; Barbu, Andrei. - : Center for Brains, Minds and Machines (CBMM), The Ninth International Conference on Learning Representations (ICLR), 2020 BASE Show details 18 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details 19 Universal Dependencies 2.4 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019 BASE Show details 20 Universal Dependencies 2.3 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details Page: 1 2 3

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