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EDA: easy data augmentation techniques for boosting performance on text classification tasks
Original
Jason W. Wei and Kai Zou. 2019b · 1901
Earlier work this paper cites.
Unsupervised data augmentation
Original
Qizhe Xie, Zihang Dai, Eduard H. Hovy, Minh-Thang Luong, and Quoc V. Le. 2019 · 1904
Earlier work this paper cites.
Robust zero-shot cross-domain slot filling with example values
Original
Darsh J. Shah, Raghav Gupta, Amir A. Fayazi, and Dilek Hakkani-Tür. 2019 · 1906
Earlier work this paper cites.
Syntax-infused transformer and BERT models for machine translation and natural language understanding
Original
Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang, Shijing Si, Dinghan Shen, Dong Wang, and Lawrence Carin. 2019 · 1911
Earlier work this paper cites.
The ATIS spoken language systems pilot corpus
Charles T. Hemphill, John J. Godfrey, and George R. Doddington. 1990 · 1990
Earlier work this paper cites.
Supervised contrastive learning
Original
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2004
Earlier work this paper cites.
Improving neural machine translation models with monolingual data
Original
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015 · 2015
Earlier work this paper cites.
Mining user intentions from medical queries: A neural network based heterogeneous jointly modeling approach
Chenwei Zhang, Wei Fan, Nan Du, and Philip S. Yu. 2016 · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.

Similar

Prototypical networks for few-shot learning
Original
Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017 · 2017
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Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces
Original
Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Maël Primet, and Joseph Dureau. 2018 · 2018
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Understanding back-translation at scale
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Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
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A deep learning based multi-task ensemble model for intent detection and slot filling in spoken language understanding
Mauajama Firdaus, Shobhit Bhatnagar, Asif Ekbal, and Pushpak Bhattacharyya. 2018 · 2018
Cited alongside, same era.

Then

Learning to classify intents and slot labels given a handful of examples
Original
Jason Krone, Yi Zhang, and Mona Diab. 2020 · 2020
Later among the works it cites.
Task augmentation by rotating for meta-learning
Original
Jialin Liu, Fei Chao, and Chih-Min Lin. 2020 · 2020
Later among the works it cites.
Data augmentation for meta-learning
Original
Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, and Tom Goldstein. 2020 · 2020
Later among the works it cites.
Meta-learning requires meta-augmentation
Original
Janarthanan Rajendran, Alex Irpan, and Eric Jang. 2020 · 2020
Later among the works it cites.
Intention detection based on siamese neural network with triplet loss
F. Ren and S. Xue. 2020 · 2020

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2021

Semi-Supervised Few-Shot Intent Classification and Slot Filling

Basu, Samyadeep, Chong, Karine lp Kiun, Sharaf, Amr et al.

Understand

Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems.

Collecting and annotating large amounts of data to train deep learning models for such systems is not scalable.
This problem can be addressed by learning from few examples using fast supervised meta-learning techniques such as prototypical networks.
In this work, we systematically investigate how contrastive learning and unsupervised data augmentation methods can benefit these existing supervised meta-learning pipelines for jointly modelled IC/SF tasks.

Built on

EDA: easy data augmentation techniques for boosting performance on text classification tasks
Original
Jason W. Wei and Kai Zou. 2019b · 1901
Earlier work this paper cites.
Unsupervised data augmentation
Original
Qizhe Xie, Zihang Dai, Eduard H. Hovy, Minh-Thang Luong, and Quoc V. Le. 2019 · 1904
Earlier work this paper cites.
Robust zero-shot cross-domain slot filling with example values
Original
Darsh J. Shah, Raghav Gupta, Amir A. Fayazi, and Dilek Hakkani-Tür. 2019 · 1906
Earlier work this paper cites.
Syntax-infused transformer and BERT models for machine translation and natural language understanding
Original
Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang, Shijing Si, Dinghan Shen, Dong Wang, and Lawrence Carin. 2019 · 1911
Earlier work this paper cites.
The ATIS spoken language systems pilot corpus
Charles T. Hemphill, John J. Godfrey, and George R. Doddington. 1990 · 1990
Earlier work this paper cites.
Supervised contrastive learning
Original
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2004
Earlier work this paper cites.
Improving neural machine translation models with monolingual data
Original
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015 · 2015
Earlier work this paper cites.
Mining user intentions from medical queries: A neural network based heterogeneous jointly modeling approach
Chenwei Zhang, Wei Fan, Nan Du, and Philip S. Yu. 2016 · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.

Similar

Prototypical networks for few-shot learning
Original
Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017 · 2017
Cited alongside, same era.
Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces
Original
Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Maël Primet, and Joseph Dureau. 2018 · 2018
Cited alongside, same era.
Understanding back-translation at scale
Original
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
Cited alongside, same era.
A deep learning based multi-task ensemble model for intent detection and slot filling in spoken language understanding
Mauajama Firdaus, Shobhit Bhatnagar, Asif Ekbal, and Pushpak Bhattacharyya. 2018 · 2018
Cited alongside, same era.

Then

Learning to classify intents and slot labels given a handful of examples
Original
Jason Krone, Yi Zhang, and Mona Diab. 2020 · 2020
Later among the works it cites.
Task augmentation by rotating for meta-learning
Original
Jialin Liu, Fei Chao, and Chih-Min Lin. 2020 · 2020
Later among the works it cites.
Data augmentation for meta-learning
Original
Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, and Tom Goldstein. 2020 · 2020
Later among the works it cites.
Meta-learning requires meta-augmentation
Original
Janarthanan Rajendran, Alex Irpan, and Eric Jang. 2020 · 2020
Later among the works it cites.
Intention detection based on siamese neural network with triplet loss
F. Ren and S. Xue. 2020 · 2020

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Marian: Fast neural machine translation in C++

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Eda: Easy data augmentation techniques for boosting performance on text classification tasks

Original

Jason Wei and Kai Zou. 2019a

Cited in the paper.

Later among the works it cites.

Meta-dataset: A dataset of datasets for learning to learn from few examples

Original

Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle. 2020 · 2020

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Encoding syntactic knowledge in transformer encoder for intent detection and slot filling

Original

Jixuan Wang, Kai Wei, Martin Radfar, Weiwei Zhang, and Clement Chung. 2020 · 2020

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Neural data augmentation via example extrapolation

Original

Kenton Lee, Kelvin Guu, Luheng He, Tim Dozat, and Hyung Won Chung. 2021 · 2021

Closest in time.