Built on

Good-enough compositional data augmentation
Original
Jacob Andreas. 2019 · 1904
Earlier work this paper cites.
Named entity recognition on code-switched data: Overview of the calcs 2018 shared task
Original
Gustavo Aguilar, Fahad AlGhamdi, Victor Soto, Mona Diab, Julia Hirschberg, and Thamar Solorio. 2019 · 1906
Earlier work this paper cites.
Overview for the second shared task on language identification in code-switched data
Original
Giovanni Molina, Fahad AlGhamdi, Mahmoud Ghoneim, Abdelati Hawwari, Nicolas Rey-Villamizar, Mona Diab, and Thamar Solorio. 2019 · 1909
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Original
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019 · 1910
Earlier work this paper cites.
Language models are few-shot learners
Original
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2005
Earlier work this paper cites.
Learning to recombine and resample data for compositional generalization
Original
Ekin Akyürek, Afra Feyza Akyürek, and Jacob Andreas. 2020 · 2010
Earlier work this paper cites.
Low-resource domain adaptation for compositional task-oriented semantic parsing
Original
Xilun Chen, Asish Ghoshal, Yashar Mehdad, Luke Zettlemoyer, and Sonal Gupta. 2020 · 2010
Earlier work this paper cites.
Update frequently, update fast: Retraining semantic parsing systems in a fraction of time
Original
Vladislav Lialin, Rahul Goel, Andrey Simanovsky, Anna Rumshisky, and Rushin Shah. 2020 · 2010
Earlier work this paper cites.
Compositional generalization and natural language variation: Can a semantic parsing approach handle both?
Original
Peter Shaw, Ming-Wei Chang, Panupong Pasupat, and Kristina Toutanova. 2020 · 2010
Earlier work this paper cites.
mt5: A massively multilingual pre-trained text-to-text transformer
Original
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2020 · 2010
Earlier work this paper cites.
Data recombination for neural semantic parsing
Original
Robin Jia and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Shallow parsing pipeline for hindi-english code-mixed social media text
Original
Arnav Sharma, Sakshi Gupta, Raveesh Motlani, Piyush Bansal, Manish Srivastava, Radhika Mamidi, and Dipti M Sharma. 2016 · 2016
Earlier work this paper cites.

Similar

Multilingual semantic parsing and code-switching
Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip R Cohen, and Mark Johnson. 2017 · 2017
Cited alongside, same era.
Universal dependency parsing for hindi-english code-switching
Original
Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava, and Dipti Misra Sharma. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Semantic parsing for task oriented dialog using hierarchical representations
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Sonal Gupta, Rushin Shah, Mrinal Mohit, Anuj Kumar, and Mike Lewis. 2018 · 2018
Cited alongside, same era.
Language modeling for code-mixing: The role of linguistic theory based synthetic data
Adithya Pratapa, Gayatri Bhat, Monojit Choudhury, Sunayana Sitaram, Sandipan Dandapat, and Kalika Bali. 2018 · 2018

Then

Not enough data? deep learning to the rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, N. Tepper, and Naama Zwerdling. 2020 · 2020
Later among the works it cites.
Style variation as a vantage point for code-switching
Khyathi Raghavi Chandu and Alan W. Black. 2020 · 2020
Later among the works it cites.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Later among the works it cites.
Don’t parse, generate! a sequence to sequence architecture for task-oriented semantic parsing
Subendhu Rongali, Luca Soldaini, Emilio Monti, and Wael Hamza. 2020 · 2020
Later among the works it cites.
Graph-based decoding for task oriented semantic parsing
Original
Jeremy R Cole, Nanjiang Jiang, Panupong Pasupat, Luheng He, and Peter Shaw. 2021 · 2021
Later among the works it cites.

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2022

CST5: Data Augmentation for Code-Switched Semantic Parsing

Agarwal, Anmol, Gupta, Jigar, Goel, Rahul et al.

Understand

Extending semantic parsers to code-switched input has been a challenging problem, primarily due to a lack of supervised training data.

In this work, we introduce CST5, a new data augmentation technique that finetunes a T5 model using a small seed set ($\approx$100 utterances) to generate code-switched utterances from English utterances.
We show that CST5 generates high quality code-switched data, both intrinsically (per human evaluation) and extrinsically by comparing baseline models which are trained without data augmentation to models which are trained with augmented data.
Empirically we observe that using CST5, one can achieve the same semantic parsing performance by using up to 20x less labeled data.

Built on

Good-enough compositional data augmentation
Original
Jacob Andreas. 2019 · 1904
Earlier work this paper cites.
Named entity recognition on code-switched data: Overview of the calcs 2018 shared task
Original
Gustavo Aguilar, Fahad AlGhamdi, Victor Soto, Mona Diab, Julia Hirschberg, and Thamar Solorio. 2019 · 1906
Earlier work this paper cites.
Overview for the second shared task on language identification in code-switched data
Original
Giovanni Molina, Fahad AlGhamdi, Mahmoud Ghoneim, Abdelati Hawwari, Nicolas Rey-Villamizar, Mona Diab, and Thamar Solorio. 2019 · 1909
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Original
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019 · 1910
Earlier work this paper cites.
Language models are few-shot learners
Original
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2005
Earlier work this paper cites.
Learning to recombine and resample data for compositional generalization
Original
Ekin Akyürek, Afra Feyza Akyürek, and Jacob Andreas. 2020 · 2010
Earlier work this paper cites.
Low-resource domain adaptation for compositional task-oriented semantic parsing
Original
Xilun Chen, Asish Ghoshal, Yashar Mehdad, Luke Zettlemoyer, and Sonal Gupta. 2020 · 2010
Earlier work this paper cites.
Update frequently, update fast: Retraining semantic parsing systems in a fraction of time
Original
Vladislav Lialin, Rahul Goel, Andrey Simanovsky, Anna Rumshisky, and Rushin Shah. 2020 · 2010
Earlier work this paper cites.
Compositional generalization and natural language variation: Can a semantic parsing approach handle both?
Original
Peter Shaw, Ming-Wei Chang, Panupong Pasupat, and Kristina Toutanova. 2020 · 2010
Earlier work this paper cites.
mt5: A massively multilingual pre-trained text-to-text transformer
Original
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2020 · 2010
Earlier work this paper cites.
Data recombination for neural semantic parsing
Original
Robin Jia and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Shallow parsing pipeline for hindi-english code-mixed social media text
Original
Arnav Sharma, Sakshi Gupta, Raveesh Motlani, Piyush Bansal, Manish Srivastava, Radhika Mamidi, and Dipti M Sharma. 2016 · 2016
Earlier work this paper cites.

Similar

Multilingual semantic parsing and code-switching
Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip R Cohen, and Mark Johnson. 2017 · 2017
Cited alongside, same era.
Universal dependency parsing for hindi-english code-switching
Original
Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava, and Dipti Misra Sharma. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Original
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Semantic parsing for task oriented dialog using hierarchical representations
Original
Sonal Gupta, Rushin Shah, Mrinal Mohit, Anuj Kumar, and Mike Lewis. 2018 · 2018
Cited alongside, same era.
Language modeling for code-mixing: The role of linguistic theory based synthetic data
Adithya Pratapa, Gayatri Bhat, Monojit Choudhury, Sunayana Sitaram, Sandipan Dandapat, and Kalika Bali. 2018 · 2018

Then

Not enough data? deep learning to the rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, N. Tepper, and Naama Zwerdling. 2020 · 2020
Later among the works it cites.
Style variation as a vantage point for code-switching
Khyathi Raghavi Chandu and Alan W. Black. 2020 · 2020
Later among the works it cites.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Later among the works it cites.
Don’t parse, generate! a sequence to sequence architecture for task-oriented semantic parsing
Subendhu Rongali, Luca Soldaini, Emilio Monti, and Wael Hamza. 2020 · 2020
Later among the works it cites.
Graph-based decoding for task oriented semantic parsing
Original
Jeremy R Cole, Nanjiang Jiang, Panupong Pasupat, Luheng He, and Peter Shaw. 2021 · 2021
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

Code-switching sentence generation by generative adversarial networks and its application to data augmentation

Ching-Ting Chang, Shun-Po Chuang, and Hung yi Lee. 2019 · 2019

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A closer look at feature space data augmentation for few-shot intent classification

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Linguistically motivated parallel data augmentation for code-switch language modeling

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Language models are unsupervised multitask learners

Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019

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El volumen louder por favor: Code-switching in task-oriented semantic parsing

Original

Arash Einolghozati, Abhinav Arora, Lorena Sainz-Maza Lecanda, Anuj Kumar, and Sonal Gupta. 2021 · 2021