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Community Development of the SWEET Semantic System for Earth and Environmental Data - A Call for Interest ...
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Community Development of the SWEET Semantic System for Earth and Environmental Data - A Call for Interest ...
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A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images ...
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A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images ...
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Synthesizing controversial sentences for testing the brain-predictivity of language models
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Typology-aware neural dependency parsing : challenges and directions
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Abstract:
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. ; Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020 ; Cataloged from student-submitted PDF version of thesis. ; Includes bibliographical references (pages 39-41). ; This thesis explores the task of leveraging typology in the context of cross-lingual dependency parsing. While this linguistic information has shown great promise in pre-neural parsing, results for neural architectures have been mixed. The aim of the investigation put forth in this thesis is to better understand this state-of-the-art. Our main findings are as follows: 1) The benefit of typological information is derived from coarsely grouping languages into syntactically-homogeneous clusters rather than from learning to leverage variations along individual typological dimensions in a compositional manner; 2) Typology consistent with the actual corpus statistics yields better transfer performance; 3) Typological similarity is only a rough proxy of cross-lingual transferability with respect to parsing. Code for the work in this thesis is available at https://github.com/ajfisch/TypologyParser. ; by Adam Fisch. ; S.M. ; S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Keyword:
Electrical Engineering and Computer Science
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URL: https://hdl.handle.net/1721.1/128400
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Speech processing with less supervision : learning from weak labels and multiple modalities
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| 12 |
Structure and geometry in sequence-processing neural networks
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| 14 |
Understanding learner engagement and the effect of course structure in massive open online courses
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| 15 |
Identifying patterns of learning : a case study of MIT's Introductory Programming Course (6.000x)
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| 16 |
Deep learning for sentiment and event-driven REIT price dynamics
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Multi-modal reinforcement learning with videogame audio to learn sonic features
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| 18 |
Automatic Analysis of Language Use in K-16 STEM Education and Impact on Student Performance
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Sign Language Translator
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In: Lo, Brandon; Mohanraj, Harshithaa; Ngo, Brandon; & Phan, Daniel. (2019). Sign Language Translator. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/4d83d182 (2019)
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