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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 Abstract: Thesis (Ph.D.)--University of Washington, 2015 ; Automatic speech recognition (ASR), the transcription of human speech into text form, is used in many settings in our society, ranging from customer service applications to personal assistants on mobile devices. In all such settings it is important for the system to know when it is making errors, so that it may ask the user to rephrase or restate their previous utterance. Such errors are often syntactically anomalous. The primary goal of this thesis is to find novel uses of parsing for automatic detection and correction of ASR errors. We start by developing a framework for ASR rescoring and automatic error detection leveraging syntactic parsing in conjunction with a maximum entropy classifier, and find that parsing helps with error detection, even when the parser is trained on out-of-domain data. In particular, features capturing parser reliability are used to improve the detection of out-of-vocabulary (OOV) and name errors. However, parsers trained on out-of-domain treebanks do not provide any benefit to ASR rescoring. This observation motivates our work on domain adaptation of parsing, with the objective of directly improving both transcription accuracy and error detection. We develop two weakly supervised domain adaptation methods which use error labels, but no hand-annotated parses: a self-training approach to directly improve the probabilistic context-free grammar (PCFG) model used in parsing, as well as a novel model combination method using a discriminative log-linear model to augment the generative PCFG. We apply both methods to ASR rescoring and error detection tasks. We find that self-training improves the ability of our parser to select the correct ASR hypothesis. The log-linear adaptation improves both OOV and name error detection tasks, and self-training performed after log-linear adaptation further improves the reliability of the parser, while producing smaller, faster models. Finally, motivated by empirical observations that the presence of names in an utterance is often indicated by words located far apart from the names themselves, we develop a general long-distance phrase pattern learning algorithm using word-level semantic similarity measures, and apply it to the problem of name error detection. This novel feature learning method leads to more robust classification, both when used independently of parsing, and in conjunction with parse features. Keyword: Electrical engineering; feature learning; machine learning; parsing; speech recognition URL: http://hdl.handle.net/1773/33149 BASE Hide details 11 Automatic Characterization of Text Difficulty Medero, Julie. - 2014 BASE Show 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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