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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 22 1 Substructure Substitution: Structured Data Augmentation for NLP ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Gimpel, Kevin; Livescu, Karen. - : Underline Science Inc., 2021 BASE Show details 2 On Generalization in Coreference Resolution ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Gimpel, Kevin; Livescu, Karen. - : Underline Science Inc., 2021 BASE Show details 3 Exemplar-Controllable Paraphrasing and Translation using Bitext ... Chen, Mingda; Wiseman, Sam; Gimpel, Kevin. - : arXiv, 2020 BASE Show details 4 Deep Clustering of Text Representations for Supervision-free Probing of Syntax ... Gupta, Vikram; Shi, Haoyue; Gimpel, Kevin. - : arXiv, 2020 BASE Show details 5 Mining Knowledge for Natural Language Inference from Wikipedia Categories ... Chen, Mingda; Chu, Zewei; Stratos, Karl. - : arXiv, 2020 BASE Show details 6 Beyond BLEU: Training Neural Machine Translation with Semantic Similarity ... Wieting, John; Berg-Kirkpatrick, Taylor; Gimpel, Kevin. - : arXiv, 2019 BASE Show details 7 Understanding stories via event sequence modeling Peng, Haoruo. - 2018 Abstract: Understanding stories, i.e. sequences of events, is a crucial yet challenging natural language understanding (NLU) problem, which requires dealing with multiple aspects of semantics, including actions, entities and emotions, as well as background knowledge. In this thesis, towards the goal of building a NLU system that can model what has happened in stories and predict what would happen in the future, we contribute on three fronts: First, we investigate the optimal way to model events in text; Second, we study how we can model a sequence of events with the balance of generality and specificity; Third, we improve event sequence modeling by joint modeling of semantic information and incorporating background knowledge. Each of the above three research problems poses both conceptual and computational challenges. For event extraction, we find that Semantic Role Labeling (SRL) signals can be served as good intermediate representations for events, thus giving us the ability to reliably identify events with minimal supervision. In addition, since it is important to resolve co-referred entities for extracted events, we make improvements to an existing co-reference resolution system. To model event sequences, we start from studying within document event co-reference (the simplest flow of events); and then extend to model two other more natural event sequences along with discourse phenomena while abstracting over the specific mentions of predicates and entities. We further identify problems for the basic event sequence models, where we fail to capture multiple semantic aspects and background knowledge. We then improve our system by jointly modeling frames, entities and sentiments, yielding joint representations of all these semantic aspects; while at the same time incorporate explicit background knowledge acquired from other corpus as well as human experience. For all tasks, we evaluate the developed algorithms and models on benchmark datasets and achieve better performance compared to other highly competitive methods. Keyword: discourse understanding; entity co-reference; event co-reference; event extraction; event sequence modeling; semantic language model URL: http://hdl.handle.net/2142/101526 BASE Hide details 8 Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext ... Wieting, John; Mallinson, Jonathan; Gimpel, Kevin. - : arXiv, 2017 BASE Show details 9 A Study of All-Convolutional Encoders for Connectionist Temporal Classification ... Krishna, Kalpesh; Lu, Liang; Gimpel, Kevin. - : arXiv, 2017 BASE Show details 10 Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information ... Tran, Trang; Toshniwal, Shubham; Bansal, Mohit. - : arXiv, 2017 BASE Show details 11 Efficient Segmental Cascades for Speech Recognition ... Tang, Hao; Wang, Weiran; Gimpel, Kevin. - : arXiv, 2016 BASE Show details 12 Discriminative Segmental Cascades for Feature-Rich Phone Recognition ... Tang, Hao; Wang, Weiran; Gimpel, Kevin. - : arXiv, 2015 BASE Show details 13 Phrase Dependency Machine Translation with Quasi-Synchronous Tree-to-Tree Features ... Gimpel, Kevin; Smith, Noah A.. - : Carnegie Mellon University, 2014 BASE Show details 14 Phrase Dependency Machine Translation with Quasi-Synchronous Tree-to-Tree Features ... Gimpel, Kevin; Smith, Noah A.. - : Carnegie Mellon University, 2014 BASE Show details 15 Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters ... Olutobi Owoputi; O'Connor, Brendan; Dyer, Chris. - : Carnegie Mellon University, 2013 BASE Show details 16 Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters ... Olutobi Owoputi; O'Connor, Brendan; Dyer, Chris. - : Carnegie Mellon University, 2013 BASE Show details 17 Concavity and Initialization for Unsupervised Dependency Parsing ... Gimpel, Kevin; Smith, Noah A.. - : Carnegie Mellon University, 2012 BASE Show details 18 Concavity and Initialization for Unsupervised Dependency Parsing ... Gimpel, Kevin; Smith, Noah A.. - : Carnegie Mellon University, 2012 BASE Show details 19 Generative Models of Monolingual and Bilingual Gappy Patterns ... Gimpel, Kevin; Smith, Noah A.. - : Carnegie Mellon University, 2011 BASE Show details 20 Generative Models of Monolingual and Bilingual Gappy Patterns ... Gimpel, Kevin; Smith, Noah A.. - : Carnegie Mellon University, 2011 BASE Show details Page: 1 2

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