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Mapping Natural Language Instructions to Mobile UI Action Sequences ...
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PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification ...
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Aspect-augmented Adversarial Networks for Domain Adaptation
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In: MIT Press (2019)
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Neuropathology of RAN translation proteins in fragile X-associated tremor/ataxia syndrome
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Neuropathology of RAN translation proteins in fragile X-associated tremor/ataxia syndrome
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The Fabric of Entropy: A Discussion on the Meaning of Fractional Information
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A Fast, Compact, Accurate Model for Language Identification of Codemixed Text ...
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Anticipating Correlative Thinking: A Comparative Analysis of the Laozi and Phaedrus
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Zhang, Yuan. - : University of Alberta. Department of East Asian Studies., 2018
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Transfer learning for low-resource natural language analysis
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Abstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. ; This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. ; Cataloged from student-submitted PDF version of thesis. ; Includes bibliographical references (pages 131-142). ; Expressive machine learning models such as deep neural networks are highly effective when they can be trained with large amounts of in-domain labeled training data. While such annotations may not be readily available for the target task, it is often possible to find labeled data for another related task. The goal of this thesis is to develop novel transfer learning techniques that can effectively leverage annotations in source tasks to improve performance of the target low-resource task. In particular, we focus on two transfer learning scenarios: (1) transfer across languages and (2) transfer across tasks or domains in the same language. In multilingual transfer, we tackle challenges from two perspectives. First, we show that linguistic prior knowledge can be utilized to guide syntactic parsing with little human intervention, by using a hierarchical low-rank tensor method. In both unsupervised and semi-supervised transfer scenarios, this method consistently outperforms state-of-the-art multilingual transfer parsers and the traditional tensor model across more than ten languages. Second, we study lexical-level multilingual transfer in low-resource settings. We demonstrate that only a few (e.g., ten) word translation pairs suffice for an accurate transfer for part-of-speech (POS) tagging. Averaged across six languages, our approach achieves a 37.5% improvement over the monolingual top-performing method when using a comparable amount of supervision. In the second monolingual transfer scenario, we propose an aspect-augmented adversarial network that allows aspect transfer over the same domain. We use this method to transfer across different aspects in the same pathology reports, where traditional domain adaptation approaches commonly fail. Experimental results demonstrate that our approach outperforms different baselines and model variants, yielding a 24% gain on this pathology dataset. ; by Yuan Zhang. ; Ph. D.
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Keyword:
Electrical Engineering and Computer Science
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URL: http://hdl.handle.net/1721.1/108847
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Ten pairs to tag - Multilingual POS tagging via coarse mapping between embeddings
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In: MIT Web Domain (2016)
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High-order low-rank tensors for semantic role labeling
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In: MIT Web Domain (2015)
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Hierarchical Low-Rank Tensors for Multilingual Transfer Parsing
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In: MIT Web Domain (2015)
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Randomized greedy inference for joint segmentation, POS tagging and dependency parsing
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In: MIT Web Domain (2015)
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Antonymous Adjectives in Disyllabic Lexical Compounds in Mandarin: A Cognitive Linguistics Perspective
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Low-Rank Tensors for Scoring Dependency Structures
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In: MIT web domain (2014)
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Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees
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In: MIT web domain (2014)
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Spatial Representation of Topological Concepts IN and ON: A Comparative Study of English and Mandarin Chinese
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