Tristan Thrush
Tristan Thrush
Facebook AI Research
Email verificata su - Home page
Citata da
Citata da
Dynabench: Rethinking benchmarking in NLP
D Kiela, M Bartolo, Y Nie, D Kaushik, A Geiger, Z Wu, B Vidgen, G Prasad, ...
NAACL, 2021
Learning from the worst: Dynamically generated datasets to improve online hate detection
B Vidgen, T Thrush, Z Waseem, D Kiela
ACL, 2021
Anlizing the adversarial natural language inference dataset
A Williams, T Thrush, D Kiela
SCiL, 2022
Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking
Z Ma*, K Ethayarajh*, T Thrush*, S Jain, L Wu, R Jia, C Potts, A Williams, ...
NeurIPS, 2021
Human-adversarial visual question answering
S Sheng, A Singh, V Goswami, JAL Magana, T Thrush, W Galuba, ...
NeurIPS, 2021
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation
M Bartolo, T Thrush, R Jia, S Riedel, P Stenetorp, D Kiela
EMNLP, 2021
The partial mental state inducer: Learning intuition with few training examples and K-line theory
T Thrush, P Winston
Advances in Cognitive Systems, 2018
Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization
T Thrush, E Wilcox, R Levy
BlackboxNLP at EMNLP, 2020
Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hate
HR Kirk, B Vidgen, P Rttger, T Thrush, SA Hale
arXiv preprint arXiv:2108.05921, 2021
Findings of the WMT 2021 Shared Task on Large-Scale Multilingual Machine Translation
G Wenzek, V Chaudhary, A Fan, S Gomez, N Goyal, S Jain, D Kiela, ...
WMT at EMNLP, 2021
Compositional neural machine translation by removing the lexicon from syntax
T Thrush
CogSci, 2020
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants
M Bartolo, T Thrush, S Riedel, P Stenetorp, R Jia, D Kiela
arXiv preprint arXiv:2112.09062, 2021
Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching
TH Pham, W Seto, S Daftry, B Ridge, J Hansen, T Thrush, ...
IEEE Robotics and Automation Letters 6 (2), 4009-4016, 2021
SAL: a Self-Aware Learning system
TAF Thrush
Massachusetts Institute of Technology, 2019
A Computational Model for Unsupervised Vowel Acquisition
T Thrush
NECPhon, 2018
Convolutions inspired by the human retina enable learning of more robust features
T Thrush
Statistical Learning Theory final project, MIT, 2018
A Self-Aware and Hypothetical Question-Answering BHPN with Drake Control
T Thrush
Probabilistic Lattice Learning and Backward Chaining
T Thrush
A Neural Model for Learning a Humanlike Vowel Feature Space
T Thrush
Il sistema al momento non pu eseguire l'operazione. Riprova pi tardi.
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