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John Wieting
John Wieting
Google Research
Bestätigte E-Mail-Adresse bei ttic.edu
Titel
Zitiert von
Zitiert von
Jahr
Palm 2 technical report
R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ...
arXiv preprint arXiv:2305.10403, 2023
9432023
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
7742023
Adversarial example generation with syntactically controlled paraphrase networks
M Iyyer, J Wieting, K Gimpel, L Zettlemoyer
arXiv preprint arXiv:1804.06059, 2018
7262018
Towards universal paraphrastic sentence embeddings
J Wieting, M Bansal, K Gimpel, K Livescu
arXiv preprint arXiv:1511.08198, 2015
7162015
ParaNMT-50M: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations
J Wieting, K Gimpel
arXiv preprint arXiv:1711.05732, 2017
3372017
From paraphrase database to compositional paraphrase model and back
J Wieting, M Bansal, K Gimpel, K Livescu
Transactions of the Association for Computational Linguistics 3, 345-358, 2015
3182015
Charagram: Embedding words and sentences via character n-grams
J Wieting, M Bansal, K Gimpel, K Livescu
arXiv preprint arXiv:1607.02789, 2016
2492016
Reformulating unsupervised style transfer as paraphrase generation
K Krishna, J Wieting, M Iyyer
arXiv preprint arXiv:2010.05700, 2020
2282020
Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
JH Clark, D Garrette, I Turc, J Wieting
Transactions of the Association for Computational Linguistics 10, 73-91, 2022
1762022
Beyond BLEU: training neural machine translation with semantic similarity
J Wieting, T Berg-Kirkpatrick, K Gimpel, G Neubig
arXiv preprint arXiv:1909.06694, 2019
1602019
Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
K Krishna, Y Song, M Karpinska, J Wieting, M Iyyer
Advances in Neural Information Processing Systems 36, 2024
1302024
compare-mt: A tool for holistic comparison of language generation systems
G Neubig, ZY Dou, J Hu, P Michel, D Pruthi, X Wang, J Wieting
arXiv preprint arXiv:1903.07926, 2019
1252019
No training required: Exploring random encoders for sentence classification
J Wieting, D Kiela
arXiv preprint arXiv:1901.10444, 2019
1202019
Learning paraphrastic sentence embeddings from back-translated bitext
J Wieting, J Mallinson, K Gimpel
arXiv preprint arXiv:1706.01847, 2017
1192017
Revisiting recurrent networks for paraphrastic sentence embeddings
J Wieting, K Gimpel
arXiv preprint arXiv:1705.00364, 2017
1042017
Rankgen: Improving text generation with large ranking models
K Krishna, Y Chang, J Wieting, M Iyyer
arXiv preprint arXiv:2205.09726, 2022
482022
Simple and effective paraphrastic similarity from parallel translations
J Wieting, K Gimpel, G Neubig, T Berg-Kirkpatrick
arXiv preprint arXiv:1909.13872, 2019
462019
On learning text style transfer with direct rewards
Y Liu, G Neubig, J Wieting
arXiv preprint arXiv:2010.12771, 2020
422020
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement
H He, J Wieting, K Gimpel, J Rao, J Lin
402016
Pushing the limits of paraphrastic sentence embeddings with millions of machine translations
J Wieting, K Gimpel
arXiv preprint arXiv:1711.05732, 2017
342017
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