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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 Hits 1 – 20 of 30 1 Going deeper: Automatic short-answer grading by combining student and question models [<Journal>] Zhang, Yuan [Verfasser]; Lin, Chen [Verfasser]; Chi, Min [Verfasser] DNB Subject Category Language Show details 2 Mapping Natural Language Instructions to Mobile UI Action Sequences ... Li, Yang; He, Jiacong; Zhou, Xin. - : arXiv, 2020 BASE Show details 3 End-to-End Chinese Parsing Exploiting Lexicons ... Zhang, Yuan; Teng, Zhiyang; Zhang, Yue. - : arXiv, 2020 BASE Show details 4 PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification ... Yang, Yinfei; Zhang, Yuan; Tar, Chris. - : arXiv, 2019 BASE Show details 5 PAWS: Paraphrase Adversaries from Word Scrambling ... Zhang, Yuan; Baldridge, Jason; He, Luheng. - : arXiv, 2019 BASE Show details 6 Aspect-augmented Adversarial Networks for Domain Adaptation Zhang, Yuan; Barzilay, Regina; Jaakkola, Tommi In: MIT Press (2019) BASE Show details 7 Neuropathology of RAN translation proteins in fragile X-associated tremor/ataxia syndrome Krans, Amy; Skariah, Geena; Zhang, Yuan. - 2019 BASE Show details 8 Neuropathology of RAN translation proteins in fragile X-associated tremor/ataxia syndrome Krans, Amy; Skariah, Geena; Zhang, Yuan. - : BioMed Central, 2019 BASE Show details 9 The Fabric of Entropy: A Discussion on the Meaning of Fractional Information Zhang, Yuan. - : University of North Texas, 2019 BASE Show details 10 A Fast, Compact, Accurate Model for Language Identification of Codemixed Text ... Zhang, Yuan; Riesa, Jason; Gillick, Daniel. - : arXiv, 2018 BASE Show details 11 Anticipating Correlative Thinking: A Comparative Analysis of the Laozi and Phaedrus Zhang, Yuan. - : University of Alberta. Department of East Asian Studies., 2018 BASE Show details 12 Transfer learning for low-resource natural language analysis Zhang, Yuan, Ph. D. Massachusetts Institute of Technology. - : Massachusetts Institute of Technology, 2017 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. Keyword: Electrical Engineering and Computer Science URL: http://hdl.handle.net/1721.1/108847 BASE Hide details 13 Ten pairs to tag - Multilingual POS tagging via coarse mapping between embeddings Gaddy, David M.; Zhang, Yuan; Barzilay, Regina... In: MIT Web Domain (2016) BASE Show details 14 High-order low-rank tensors for semantic role labeling Lei, Tao; Zhang, Yuan; Marquez, Lluis... In: MIT Web Domain (2015) BASE Show details 15 Hierarchical Low-Rank Tensors for Multilingual Transfer Parsing Barzilay, Regina; Zhang, Yuan In: MIT Web Domain (2015) BASE Show details 16 Randomized greedy inference for joint segmentation, POS tagging and dependency parsing Zhang, Yuan; Li, Chengtao; Barzilay, Regina... In: MIT Web Domain (2015) BASE Show details 17 Antonymous Adjectives in Disyllabic Lexical Compounds in Mandarin: A Cognitive Linguistics Perspective Zhang, Yuan; Kemmer, Suzanne. - : Horizon Research Publishing,USA, 2015 BASE Show details 18 Low-Rank Tensors for Scoring Dependency Structures Lei, Tao; Zhang, Yuan; Barzilay, Regina... In: MIT web domain (2014) BASE Show details 19 Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees Zhang, Yuan; Lei, Tao; Barzilay, Regina... In: MIT web domain (2014) BASE Show details 20 Spatial Representation of Topological Concepts IN and ON: A Comparative Study of English and Mandarin Chinese Zhang, Yuan. - 2013 BASE Show details Page: 1 2

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