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Not Enough Data? Deep Learning to the Rescue!
v1v2 (latest)

Not Enough Data? Deep Learning to the Rescue!

AAAI Conference on Artificial Intelligence (AAAI), 2019
8 November 2019
Ateret Anaby-Tavor
Boaz Carmeli
Esther Goldbraich
Amir Kantor
George Kour
Segev Shlomov
N. Tepper
Naama Zwerdling
ArXiv (abs)PDFHTML

Papers citing "Not Enough Data? Deep Learning to the Rescue!"

19 / 169 papers shown
Title
A Survey on Data Augmentation for Text Classification
A Survey on Data Augmentation for Text Classification
Markus Bayer
M. Kaufhold
Christian A. Reuter
399
415
0
07 Jul 2021
An Empirical Survey of Data Augmentation for Limited Data Learning in
  NLP
An Empirical Survey of Data Augmentation for Limited Data Learning in NLPTransactions of the Association for Computational Linguistics (TACL), 2021
Jiaao Chen
Derek Tam
Colin Raffel
Joey Tianyi Zhou
Diyi Yang
224
207
0
14 Jun 2021
Summary Grounded Conversation Generation
Summary Grounded Conversation GenerationFindings (Findings), 2021
R. Chulaka Gunasekara
Guy Feigenblat
Benjamin Sznajder
Sachindra Joshi
D. Konopnicki
91
9
0
07 Jun 2021
Knowing More About Questions Can Help: Improving Calibration in Question
  Answering
Knowing More About Questions Can Help: Improving Calibration in Question AnsweringFindings (Findings), 2021
Shujian Zhang
Chengyue Gong
Eunsol Choi
UQLM
229
62
0
02 Jun 2021
Joint Text and Label Generation for Spoken Language Understanding
Joint Text and Label Generation for Spoken Language Understanding
Yang Li
Ben Athiwaratkun
Cicero Nogueira dos Santos
Bing Xiang
143
0
0
11 May 2021
A Survey of Data Augmentation Approaches for NLP
A Survey of Data Augmentation Approaches for NLPFindings (Findings), 2021
Steven Y. Feng
Varun Gangal
Jason W. Wei
Sarath Chandar
Soroush Vosoughi
Teruko Mitamura
Eduard H. Hovy
AIMat
596
899
0
07 May 2021
Generating Datasets with Pretrained Language Models
Generating Datasets with Pretrained Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Timo Schick
Hinrich Schütze
324
257
0
15 Apr 2021
Consistency Training with Virtual Adversarial Discrete Perturbation
Consistency Training with Virtual Adversarial Discrete PerturbationNorth American Chapter of the Association for Computational Linguistics (NAACL), 2021
Jungsoo Park
Gyuwan Kim
Jaewoo Kang
141
15
0
15 Apr 2021
Data Augmentation in Natural Language Processing: A Novel Text
  Generation Approach for Long and Short Text Classifiers
Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text ClassifiersInternational Journal of Machine Learning and Cybernetics (IJMLC), 2021
Markus Bayer
M. Kaufhold
Björn Buchhold
Marcel Keller
J. Dallmeyer
Christian A. Reuter
184
140
0
26 Mar 2021
Neural Data Augmentation via Example Extrapolation
Neural Data Augmentation via Example Extrapolation
Kenton Lee
Kelvin Guu
Luheng He
Timothy Dozat
Hyung Won Chung
129
75
0
02 Feb 2021
DAGA: Data Augmentation with a Generation Approach for Low-resource
  Tagging Tasks
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks
Bosheng Ding
Linlin Liu
Lidong Bing
Canasai Kruengkrai
Thien Hai Nguyen
Shafiq Joty
Luo Si
Chunyan Miao
139
37
0
03 Nov 2020
A Survey on Recent Approaches for Natural Language Processing in
  Low-Resource Scenarios
A Survey on Recent Approaches for Natural Language Processing in Low-Resource ScenariosNorth American Chapter of the Association for Computational Linguistics (NAACL), 2020
Michael A. Hedderich
Lukas Lange
Heike Adel
Jannik Strötgen
Dietrich Klakow
610
341
0
23 Oct 2020
Simulated Chats for Building Dialog Systems: Learning to Generate
  Conversations from Instructions
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from InstructionsConference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Biswesh Mohapatra
Gaurav Pandey
Danish Contractor
Sachindra Joshi
219
30
0
20 Oct 2020
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic
  Training Data
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training DataConference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Silei Xu
Sina J. Semnani
Giovanni Campagna
M. Lam
191
54
0
09 Oct 2020
SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
SeqMix: Augmenting Active Sequence Labeling via Sequence MixupConference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Rongzhi Zhang
Yue Yu
Chao Zhang
VLM
212
97
0
05 Oct 2020
Tell Me How to Ask Again: Question Data Augmentation with Controllable
  Rewriting in Continuous Space
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous SpaceConference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Dayiheng Liu
Yeyun Gong
Jie Fu
Yu Yan
Jiusheng Chen
Jiancheng Lv
Nan Duan
M. Zhou
123
39
0
04 Oct 2020
On Data Augmentation for Extreme Multi-label Classification
On Data Augmentation for Extreme Multi-label Classification
Danqing Zhang
Tao Li
Hai-Feng Zhang
Bing Yin
147
28
0
22 Sep 2020
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving
  Out-of-Domain Robustness
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain RobustnessConference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Nathan Ng
Dong Wang
Marzyeh Ghassemi
195
150
0
21 Sep 2020
Multi-task learning for natural language processing in the 2020s: where
  are we going?
Multi-task learning for natural language processing in the 2020s: where are we going?
Joseph Worsham
Jugal Kalita
AIMat
156
87
0
22 Jul 2020
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