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Emily Pitler
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2020 – today
- 2024
- [i11]Jianxin Zhang, Josh Viktorov, Doosan Jung, Emily Pitler:
Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions. CoRR abs/2410.03973 (2024) - 2023
- [i10]Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Slav Petrov, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Timothy P. Lillicrap, Angeliki Lazaridou, Orhan Firat, James Molloy, Michael Isard, Paul Ronald Barham, Tom Hennigan, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, Ryan Doherty, Eli Collins, Clemens Meyer, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, George Tucker, Enrique Piqueras, Maxim Krikun, Iain Barr, Nikolay Savinov, Ivo Danihelka, Becca Roelofs, Anaïs White, Anders Andreassen, Tamara von Glehn, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, et al.:
Gemini: A Family of Highly Capable Multimodal Models. CoRR abs/2312.11805 (2023) - 2022
- [c23]Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova:
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing. EMNLP 2022: 9157-9179 - [i9]Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova:
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing. CoRR abs/2205.12253 (2022) - 2020
- [c22]Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler, Tal Linzen:
Syntactic Data Augmentation Increases Robustness to Inference Heuristics. ACL 2020: 2339-2352 - [c21]Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, Emily Pitler:
New Protocols and Negative Results for Textual Entailment Data Collection. EMNLP (1) 2020: 8203-8214 - [i8]Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, Emily Pitler:
Collecting Entailment Data for Pretraining: New Protocols and Negative Results. CoRR abs/2004.11997 (2020) - [i7]Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler, Tal Linzen:
Syntactic Data Augmentation Increases Robustness to Inference Heuristics. CoRR abs/2004.11999 (2020) - [i6]Kellie Webster, Emily Pitler:
Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation. CoRR abs/2006.08881 (2020) - [i5]Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Slav Petrov:
Measuring and Reducing Gendered Correlations in Pre-trained Models. CoRR abs/2010.06032 (2020)
2010 – 2019
- 2019
- [c20]Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, Michael Collins:
Synthetic QA Corpora Generation with Roundtrip Consistency. ACL (1) 2019: 6168-6173 - [c19]Daniel Andor, Luheng He, Kenton Lee, Emily Pitler:
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension. EMNLP/IJCNLP (1) 2019: 5946-5951 - [i4]Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, Michael Collins:
Synthetic QA Corpora Generation with Roundtrip Consistency. CoRR abs/1906.05416 (2019) - [i3]Daniel Andor, Luheng He, Kenton Lee, Emily Pitler:
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension. CoRR abs/1909.00109 (2019) - 2018
- [c18]Bernd Bohnet, Ryan T. McDonald, Gonçalo Simões, Daniel Andor, Emily Pitler, Joshua Maynez:
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings. ACL (1) 2018: 2642-2652 - [c17]Ali El-Kahky, Kellie Webster, Daniel Andor, Emily Pitler:
A Challenge Set and Methods for Noun-Verb Ambiguity. EMNLP 2018: 2562-2572 - [i2]Bernd Bohnet, Ryan T. McDonald, Gonçalo Simões, Daniel Andor, Emily Pitler, Joshua Maynez:
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings. CoRR abs/1805.08237 (2018) - 2017
- [c16]Daniel Zeman, Martin Popel, Milan Straka, Jan Hajic, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast, Francis M. Tyers, Elena Badmaeva, Memduh Gokirmak, Anna Nedoluzhko, Silvie Cinková, Jan Hajic Jr., Jaroslava Hlavácová, Václava Kettnerová, Zdenka Uresová, Jenna Kanerva, Stina Ojala, Anna Missilä, Christopher D. Manning, Sebastian Schuster, Siva Reddy, Dima Taji, Nizar Habash, Herman Leung, Marie-Catherine de Marneffe, Manuela Sanguinetti, Maria Simi, Hiroshi Kanayama, Valeria de Paiva, Kira Droganova, Héctor Martínez Alonso, Çagri Çöltekin, Umut Sulubacak, Hans Uszkoreit, Vivien Macketanz, Aljoscha Burchardt, Kim Harris, Katrin Marheinecke, Georg Rehm, Tolga Kayadelen, Mohammed Attia, Ali El-Kahky, Zhuoran Yu, Emily Pitler, Saran Lertpradit, Michael Mandl, Jesse Kirchner, Hector Fernandez Alcalde, Jana Strnadová, Esha Banerjee, Ruli Manurung, Antonio Stella, Atsuko Shimada, Sookyoung Kwak, Gustavo Mendonça, Tatiana Lando, Rattima Nitisaroj, Josie Li:
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. CoNLL Shared Task (2) 2017: 1-19 - [c15]Jan A. Botha, Emily Pitler, Ji Ma, Anton Bakalov, Alex Salcianu, David Weiss, Ryan T. McDonald, Slav Petrov:
Natural Language Processing with Small Feed-Forward Networks. EMNLP 2017: 2879-2885 - [i1]Jan A. Botha, Emily Pitler, Ji Ma, Anton Bakalov, Alex Salcianu, David Weiss, Ryan T. McDonald, Slav Petrov:
Natural Language Processing with Small Feed-Forward Networks. CoRR abs/1708.00214 (2017) - 2016
- [c14]Bernd Bohnet, Ryan T. McDonald, Emily Pitler, Ji Ma:
Generalized Transition-based Dependency Parsing via Control Parameters. ACL (1) 2016 - 2015
- [c13]Emily Pitler, Ryan T. McDonald:
A Linear-Time Transition System for Crossing Interval Trees. HLT-NAACL 2015: 662-671 - 2014
- [j2]Emily Pitler:
A Crossing-Sensitive Third-Order Factorization for Dependency Parsing. Trans. Assoc. Comput. Linguistics 2: 41-54 (2014) - 2013
- [j1]Emily Pitler, Sampath Kannan, Mitchell Marcus:
Finding Optimal 1-Endpoint-Crossing Trees. Trans. Assoc. Comput. Linguistics 1: 13-24 (2013) - 2012
- [c12]Emily Pitler:
Attacking Parsing Bottlenecks with Unlabeled Data and Relevant Factorizations. ACL (1) 2012: 768-776 - [c11]Emily Pitler, Sampath Kannan, Mitchell Marcus:
Dynamic Programming for Higher Order Parsing of Gap-Minding Trees. EMNLP-CoNLL 2012: 478-488 - 2010
- [c10]Emily Pitler, Annie Louis, Ani Nenkova:
Automatic Evaluation of Linguistic Quality in Multi-Document Summarization. ACL 2010: 544-554 - [c9]Shane Bergsma, Emily Pitler, Dekang Lin:
Creating Robust Supervised Classifiers via Web-Scale N-Gram Data. ACL 2010: 865-874 - [c8]Emily Pitler, Shane Bergsma, Dekang Lin, Kenneth Ward Church:
Using Web-scale N-grams to Improve Base NP Parsing Performance. COLING 2010: 886-894 - [c7]Ani Nenkova, Jieun Chae, Annie Louis, Emily Pitler:
Structural Features for Predicting the Linguistic Quality of Text - Applications to Machine Translation, Automatic Summarization and Human-Authored Text. Empirical Methods in Natural Language Generation 2010: 222-241 - [c6]Dekang Lin, Kenneth Ward Church, Heng Ji, Satoshi Sekine, David Yarowsky, Shane Bergsma, Kailash Patil, Emily Pitler, Rachel Lathbury, Vikram Rao, Kapil Dalwani, Sushant Narsale:
New Tools for Web-Scale N-grams. LREC 2010
2000 – 2009
- 2009
- [c5]Emily Pitler, Ani Nenkova:
Using Syntax to Disambiguate Explicit Discourse Connectives in Text. ACL/IJCNLP (2) 2009: 13-16 - [c4]Emily Pitler, Annie Louis, Ani Nenkova:
Automatic sense prediction for implicit discourse relations in text. ACL/IJCNLP 2009: 683-691 - [c3]Emily Pitler, Ken Ward Church:
Using Word-Sense Disambiguation Methods to Classify Web Queries by Intent. EMNLP 2009: 1428-1436 - 2008
- [c2]Emily Pitler, Mridhula Raghupathy, Hena Mehta, Ani Nenkova, Alan Lee, Aravind K. Joshi:
Easily Identifiable Discourse Relations. COLING (Posters) 2008: 87-90 - [c1]Emily Pitler, Ani Nenkova:
Revisiting Readability: A Unified Framework for Predicting Text Quality. EMNLP 2008: 186-195
Coauthor Index
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last updated on 2024-11-14 00:56 CET by the dblp team
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