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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 4 Hits 1 – 20 of 70 1 Automatic Dialect Density Estimation for African American English ... Johnson, Alexander; Everson, Kevin; Ravi, Vijay. - : arXiv, 2022 BASE Show details 2 DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Hajishirzi, Hannaneh; Lu, Bo-Ru. - : Underline Science Inc., 2021 BASE Show details 3 Dialogue State Tracking with a Language Model using Schema-Driven Prompting ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cheng, Hao; Lee, Chia-Hsuan. - : Underline Science Inc., 2021 BASE Show details 4 A Controllable Model of Grounded Response Generation ... Wu, Zeqiu; Galley, Michel; Brockett, Chris. - : arXiv, 2020 BASE Show details 5 Neural Models for Integrating Prosody in Spoken Language Understanding Tran, Trang. - 2020 BASE Show details 6 Automatic Analysis of Language Use in K-16 STEM Education and Impact on Student Performance Nadeem, Farah. - 2020 BASE Show details 7 Asynchronous Speech Recognition Affects Physician Editing of Notes Lybarger, Kevin J.; Ostendorf, Mari; Riskin, Eve. - : Georg Thieme Verlag KG, 2018 BASE Show details 8 Low-Rank RNN Adaptation for Context-Aware Language Modeling Jaech, Aaron. - 2018 BASE Show details 9 Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information ... Tran, Trang; Toshniwal, Shubham; Bansal, Mohit. - : arXiv, 2017 BASE Show details 10 Effective Use of Cross-Domain Parsing in Automatic Speech Recognition and Error Detection Marin, Marius. - 2015 BASE Show details 11 Automatic Characterization of Text Difficulty Medero, Julie. - 2014 Abstract: Thesis (Ph.D.)--University of Washington, 2014 ; For the millions of U.S. adults who do not read well enough to complete day-to-day tasks, challenges arise in reading news articles or employment documents, researching health conditions, and a variety of other tasks. Adults may struggle to read because they are not native speakers of English, because of learning disabilities, or simply because they did not receive sufficient reading instruction as children. In classroom settings, struggling readers can be given hand-crafted texts to read. Manual simplification is time-consuming for a teacher or other adult, though, and is not available for adults who are not in a classroom environment. In this thesis, we present a fundamentally new approach to understanding text difficulty aimed at supporting automatic text simplification. This way of thinking about what it means for a text to be "hard" is useful both in deciding what should be simplified and in deciding whether a machine-generated simplification is a good one. We start by describing a new corpus of parallel manual simplifications, with which we are able to analyze how writers perform simplification. We look at which words are replaced during simplification, and at which sentences are split into multiple simplified sentences, shortened, or expanded. We find very low agreement with respect to how the simplification task should be completed, with writers finding a variety of ways to simplify the same content. This finding motivates a new, empirical approach to characterizing difficulty. Instead of looking at human simplifications to learn what is difficult, we look at human reading performance. We use an existing, large-scale collection of oral readings to explore acoustic features during oral reading. We then leverage measurements from a new eye tracking study, finding that all hypothesized acoustic measurements are correlated with one of two features from eye tracking that are known to be related to reading difficulty. We use these human performance measures to connect text readability assessment to individual literacy assessment methods based on oral reading. Finally, we develop several text difficulty measures based on large text corpora. Using comparable documents from English and Simple English Wikipedia, we identify words that are likely to be simplified. We use a character-based language model and features from Wiktionary definitions to predict word difficulty, and show that those measures correlate with observed word difficulty rankings. We also examine sentence difficulty, identifying lexical, syntactic, and topic-based features that are useful in predicting when a sentence should be split, shortened, or expanded during simplification. We compare those predictions to empirical sentence difficulty based on oral reading, finding that lexical and syntactic features are useful in predicting difficulty, while topic-based features are useful in deciding how to simplify a difficult sentence. Keyword: Computer science; Electrical engineering; Linguistics URL: http://hdl.handle.net/1773/26151 BASE Hide details 12 Data Selection for Statistical Machine Translation Axelrod, Amittai. - 2014 BASE Show details 13 Graph-based query strategies for active learning Wu, Wei; Ostendorf, Mari In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 21 (2013) 2, 260-269 OLC Linguistik Show details 14 Rank and Sparsity in Language Processing Hutchinson, Brian. - 2013 BASE Show details 15 Joint reranking of parsing and word recognition with automatic segmentation Ostendorf, Mari; Kahn, Jeremy G. In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 26 (2012) 1, 1-19 BLLDB OLC Linguistik Show details 16 Graph-based Algorithms for Lexical Semantics and its Applications Wu, Wei. - 2012 BASE Show details 17 Expected dependency pair match: predicting translation quality with expected syntactic structure Ostendorf, Mari; Kahn, Jeremy G.; Snover, Matthew G. In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 23 (2010) 2-3, 169-179 BLLDB OLC Linguistik Show details 18 A machine learning approach to reading level assessment Petersen, Sarah E.; Ostendorf, Mari In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 23 (2009) 1, 89-106 OLC Linguistik Show details 19 A machine learning approach to reading level assessment Petersen, Sarah E.; Ostendorf, Mari In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 23 (2009) 1, 89-106 BLLDB OLC Linguistik Show details 20 Improving robustness of MLLR adaptation with speaker-clustered regression class trees Mandal, Arindam; Ostendorf, Mari; Stolcke, Andreas In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 23 (2009) 2, 176-199 BLLDB OLC Linguistik Show details Page: 1 2 3 4

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