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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 – 7 of 7 1 Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality ... Thrush, Tristan; Jiang, Ryan; Bartolo, Max. - : arXiv, 2022 BASE Show details 2 ANLIzing the Adversarial Natural Language Inference Dataset Williams, Adina; Thrush, Tristan; Kiela, Douwe In: Proceedings of the Society for Computation in Linguistics (2022) BASE Show details 3 Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization Wilcox, Ethan; Thrush, Tristan; Levy, Roger In: Association for Computational Linguistics (2021) BASE Show details 4 Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection ... 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; Kiela, Douwe; Talat, Zeerak. - : Underline Science Inc., 2021 BASE Show details 5 Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bartolo, Max; Jia, Robin. - : Underline Science Inc., 2021 BASE Show details 6 Compositional Neural Machine Translation by Removing the Lexicon from Syntax ... Thrush, Tristan. - : arXiv, 2020 BASE Show details 7 SAL : a Self-Aware Learning system ; Self-Aware Learning system Thrush, Tristan Andrew Fraser.. - : Massachusetts Institute of Technology, 2019 Abstract: This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. ; Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 ; Cataloged from student-submitted PDF of thesis. ; Includes bibliographical references (pages 67-68). ; In this thesis, I take a step towards understanding how and why humans learn to solve problems about their solving of problems. I present a general-purpose neural reinforcement learning system called SAL, which can learn to think about its own problem solving, and use this capability to learn how to solve problems at another level. I show that SAL can use self-reference to articulate, and learn to articulate, its thoughts to a human, and internalize and apply a human's help, in natural language. I also demonstrate that SAL's abilities are enabled by an internal representation that shares important properties with, and is easily converted between, natural language. On the practical side, I argue that SAL can inform production question answering systems research. SAL can answer multi-step questions that are grounded in the world by extracting operational knowledge from pre-trained word embeddings. As an example, SAL knows how to use the action associated with \grab [the] diesel jug" to get closer to a solution, given the state of a physical world and a goal. And SAL can do this without any actual experience using (and without ever being told by a human about) any action associated with \grab" or the argument \diesel jug." SAL can do so with both very little training reward data and without assuming anything about the operational meaning of a particular lexical item, or composition of them, at first. Alternatively, typical neural reinforcement learning systems can not learn like SAL; they only work with a level of data that would be difficult to achieve in the real world. SAL's implementation, trained models, analysis code, and instructions, are at https://github.com/TristanThrush/sal. It is easy to add new problems (even in new domains) that you want SAL to learn. ; by Tristan Andrew Fraser Thrush. ; M. Eng. ; M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science Keyword: Electrical Engineering and Computer Science URL: https://hdl.handle.net/1721.1/127705 BASE Hide details

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