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Tim Vieira
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- affiliation: Johns Hopkins University, Baltimore, MD, USA
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2020 – today
- 2024
- [c23]Afra Amini, Tim Vieira, Ryan Cotterell:
Direct Preference Optimization with an Offset. ACL (Findings) 2024: 9954-9972 - [c22]Mario Giulianelli, Luca Malagutti, Juan Luis Gastaldi, Brian DuSell, Tim Vieira, Ryan Cotterell:
On the Proper Treatment of Tokenization in Psycholinguistics. EMNLP 2024: 18556-18572 - [i24]Afra Amini, Tim Vieira, Ryan Cotterell:
Direct Preference Optimization with an Offset. CoRR abs/2402.10571 (2024) - [i23]Afra Amini, Tim Vieira, Ryan Cotterell:
Variational Best-of-N Alignment. CoRR abs/2407.06057 (2024) - [i22]Juan Luis Gastaldi, John Terilla, Luca Malagutti, Brian DuSell, Tim Vieira, Ryan Cotterell:
The Foundations of Tokenization: Statistical and Computational Concerns. CoRR abs/2407.11606 (2024) - [i21]Mario Giulianelli, Luca Malagutti, Juan Luis Gastaldi, Brian DuSell, Tim Vieira, Ryan Cotterell:
On the Proper Treatment of Tokenization in Psycholinguistics. CoRR abs/2410.02691 (2024) - 2023
- [c21]Vilém Zouhar, Clara Meister, Juan Luis Gastaldi
, Li Du, Tim Vieira, Mrinmaya Sachan, Ryan Cotterell:
A Formal Perspective on Byte-Pair Encoding. ACL (Findings) 2023: 598-614 - [c20]Andreas Opedal, Ran Zmigrod, Tim Vieira, Ryan Cotterell, Jason Eisner:
Efficient Semiring-Weighted Earley Parsing. ACL (1) 2023: 3687-3713 - [c19]Clemente Pasti, Andreas Opedal, Tiago Pimentel, Tim Vieira, Jason Eisner, Ryan Cotterell:
On the Intersection of Context-Free and Regular Languages. EACL 2023: 737-749 - [c18]Alexandra Butoi, Tim Vieira, Ryan Cotterell, David Chiang:
Efficient Algorithms for Recognizing Weighted Tree-Adjoining Languages. EMNLP 2023: 5394-5416 - [c17]Andreas Opedal, Eleftheria Tsipidi, Tiago Pimentel, Ryan Cotterell, Tim Vieira:
An Exploration of Left-Corner Transformations. EMNLP 2023: 13393-13427 - [i20]Anej Svete, Benjamin Dayan, Tim Vieira, Ryan Cotterell, Jason Eisner:
Algorithms for Acyclic Weighted Finite-State Automata with Failure Arcs. CoRR abs/2301.06862 (2023) - [i19]Vilém Zouhar, Clara Meister, Juan Luis Gastaldi, Li Du, Tim Vieira, Mrinmaya Sachan, Ryan Cotterell:
A Formal Perspective on Byte-Pair Encoding. CoRR abs/2306.16837 (2023) - [i18]Andreas Opedal, Ran Zmigrod, Tim Vieira, Ryan Cotterell, Jason Eisner:
Efficient Semiring-Weighted Earley Parsing. CoRR abs/2307.02982 (2023) - [i17]Alexandra Butoi, Tim Vieira, Ryan Cotterell, David Chiang:
Efficient Algorithms for Recognizing Weighted Tree-Adjoining Languages. CoRR abs/2310.15276 (2023) - [i16]Andreas Opedal, Eleftheria Tsipidi, Tiago Pimentel, Ryan Cotterell, Tim Vieira:
An Exploration of Left-Corner Transformations. CoRR abs/2311.16258 (2023) - 2022
- [c16]Anej Svete, Benjamin Dayan, Ryan Cotterell, Tim Vieira, Jason Eisner:
Algorithms for Acyclic Weighted Finite-State Automata with Failure Arcs. EMNLP 2022: 8289-8305 - [c15]Alexandra Butoi, Brian DuSell, Tim Vieira, Ryan Cotterell, David Chiang:
Algorithms for Weighted Pushdown Automata. EMNLP 2022: 9669-9680 - [c14]Ran Zmigrod, Tim Vieira, Ryan Cotterell:
Exact Paired-Permutation Testing for Structured Test Statistics. NAACL-HLT 2022: 4894-4902 - [i15]Ran Zmigrod, Tim Vieira, Ryan Cotterell:
Exact Paired-Permutation Testing for Structured Test Statistics. CoRR abs/2205.01416 (2022) - [i14]Clemente Pasti, Andreas Opedal, Tiago Pimentel, Tim Vieira, Jason Eisner, Ryan Cotterell:
On the Intersection of Context-Free and Regular Languages. CoRR abs/2209.06809 (2022) - [i13]Alexandra Butoi, Brian DuSell, Tim Vieira, Ryan Cotterell, David Chiang:
Algorithms for Weighted Pushdown Automata. CoRR abs/2210.06884 (2022) - 2021
- [j4]Ran Zmigrod, Tim Vieira, Ryan Cotterell:
Efficient Computation of Expectations under Spanning Tree Distributions. Trans. Assoc. Comput. Linguistics 9: 675-690 (2021) - [c13]Ran Zmigrod, Tim Vieira, Ryan Cotterell
Higher-order Derivatives of Weighted Finite-state Machines. ACL/IJCNLP (2) 2021: 240-248 - [c12]Ran Zmigrod, Tim Vieira, Ryan Cotterell
On Finding the K-best Non-projective Dependency Trees. ACL/IJCNLP (1) 2021: 1324-1337 - [c11]Clara Meister, Afra Amini, Tim Vieira, Ryan Cotterell:
Conditional Poisson Stochastic Beams. EMNLP (1) 2021: 664-681 - [c10]Tim Vieira, Ryan Cotterell, Jason Eisner:
Searching for More Efficient Dynamic Programs. EMNLP (Findings) 2021: 3812-3830 - [c9]Ran Zmigrod, Tim Vieira, Ryan Cotterell:
Efficient Sampling of Dependency Structure. EMNLP (1) 2021: 10558-10569 - [i12]Ran Zmigrod, Tim Vieira, Ryan Cotterell:
Higher-order Derivatives of Weighted Finite-state Machines. CoRR abs/2106.00749 (2021) - [i11]Ran Zmigrod, Tim Vieira, Ryan Cotterell:
On Finding the K-best Non-projective Dependency Trees. CoRR abs/2106.00780 (2021) - [i10]Ran Zmigrod, Tim Vieira, Ryan Cotterell:
Efficient Sampling of Dependency Structures. CoRR abs/2109.06521 (2021) - [i9]Tim Vieira, Ryan Cotterell, Jason Eisner:
Searching for More Efficient Dynamic Programs. CoRR abs/2109.06966 (2021) - [i8]Clara Meister, Afra Amini, Tim Vieira, Ryan Cotterell:
Conditional Poisson Stochastic Beam Search. CoRR abs/2109.11034 (2021) - 2020
- [j3]Clara Meister, Ryan Cotterell, Tim Vieira:
Best-First Beam Search. Trans. Assoc. Comput. Linguistics 8: 795-809 (2020) - [c8]Clara Meister, Ryan Cotterell
If beam search is the answer, what was the question? EMNLP (1) 2020: 2173-2185 - [c7]Ran Zmigrod, Tim Vieira, Ryan Cotterell:
Please Mind the Root: Decoding Arborescences for Dependency Parsing. EMNLP (1) 2020: 4809-4819 - [c6]Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha, Venkata Subrahmanyan Govindarajan, Dee Ann Reisinger, Tim Vieira, Keisuke Sakaguchi, Sheng Zhang, Francis Ferraro, Rachel Rudinger
The Universal Decompositional Semantics Dataset and Decomp Toolkit. LREC 2020: 5698-5707 - [i7]Clara Meister, Tim Vieira, Ryan Cotterell:
Best-First Beam Search. CoRR abs/2007.03909 (2020) - [i6]Ran Zmigrod, Tim Vieira, Ryan Cotterell:
Efficient Computation of Expectations under Spanning Tree Distributions. CoRR abs/2008.12988 (2020) - [i5]Ran Zmigrod, Tim Vieira, Ryan Cotterell:
Please Mind the Root: Decoding Arborescences for Dependency Parsing. CoRR abs/2010.02550 (2020) - [i4]Clara Meister, Tim Vieira, Ryan Cotterell:
If beam search is the answer, what was the question? CoRR abs/2010.02650 (2020) - [i3]Matthew Francis-Landau, Tim Vieira, Jason Eisner:
Evaluation of Logic Programs with Built-Ins and Aggregation: A Calculus for Bag Relations. CoRR abs/2010.10503 (2020)
2010 – 2019
- 2019
- [i2]Aaron Steven White, Elias Stengel-Eskin, Siddharth Vashishtha, Venkata Subrahmanyan Govindarajan, Dee Ann Reisinger, Tim Vieira, Keisuke Sakaguchi, Sheng Zhang, Francis Ferraro, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme:
The Universal Decompositional Semantics Dataset and Decomp Toolkit. CoRR abs/1909.13851 (2019) - 2017
- [j2]Tim Vieira, Jason Eisner:
Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing. Trans. Assoc. Comput. Linguistics 5: 263-278 (2017) - [c5]Tim Vieira, Matthew Francis-Landau, Nathaniel Wesley Filardo, Farzad Khorasani, Jason Eisner:
Dyna: toward a self-optimizing declarative language for machine learning applications. MAPL@PLDI 2017: 8-17 - 2016
- [c4]Aaron Steven White
Universal Decompositional Semantics on Universal Dependencies. EMNLP 2016: 1713-1723 - [c3]Tim Vieira, Ryan Cotterell, Jason Eisner:
Speed-Accuracy Tradeoffs in Tagging with Variable-Order CRFs and Structured Sparsity. EMNLP 2016: 1973-1978 - [c2]Ryan Cotterell, Tim Vieira, Hinrich Schütze:
A Joint Model of Orthography and Morphological Segmentation. HLT-NAACL 2016: 664-669 - 2015
- [j1]Subhro Roy, Tim Vieira, Dan Roth:
Reasoning about Quantities in Natural Language. Trans. Assoc. Comput. Linguistics 3: 1-13 (2015) - 2012
- [c1]Jason Naradowsky, Tim Vieira, David A. Smith:
Grammarless Parsing for Joint Inference. COLING 2012: 1995-2010
2000 – 2009
- 2009
- [i1]Mark Sammons, V. G. Vinod Vydiswaran, Tim Vieira, Nikhil Johri, Ming-Wei Chang, Dan Goldwasser, Vivek Srikumar, Gourab Kundu, Yuancheng Tu, Kevin Small, Joshua S. Rule, Quang Do, Dan Roth:
Relation Alignment for Textual Entailment Recognition. TAC 2009
Coauthor Index
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last updated on 2024-11-15 20:35 CET by the dblp team
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