@inproceedings{bandyopadhyay-zhao-2020-natural,
title = "Natural Language Response Generation from {SQL} with Generalization and Back-translation",
author = "Bandyopadhyay, Saptarashmi and
Zhao, Tianyang",
editor = "Bogin, Ben and
Iyer, Srinivasan and
Lin, Xi Victoria and
Radev, Dragomir and
Suhr, Alane and
{Panupong} and
Xiong, Caiming and
Yin, Pengcheng and
Yu, Tao and
Zhang, Rui and
Zhong, Victor",
booktitle = "Proceedings of the First Workshop on Interactive and Executable Semantic Parsing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.intexsempar-1.6",
doi = "10.18653/v1/2020.intexsempar-1.6",
pages = "46--49",
abstract = "Generation of natural language responses to the queries of structured language like SQL is very challenging as it requires generalization to new domains and the ability to answer ambiguous queries among other issues. We have participated in the CoSQL shared task organized in the IntEx-SemPar workshop at EMNLP 2020. We have trained a number of Neural Machine Translation (NMT) models to efficiently generate the natural language responses from SQL. Our shuffled back-translation model has led to a BLEU score of 7.47 on the unknown test dataset. In this paper, we will discuss our methodologies to approach the problem and future directions to improve the quality of the generated natural language responses.",
}
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<abstract>Generation of natural language responses to the queries of structured language like SQL is very challenging as it requires generalization to new domains and the ability to answer ambiguous queries among other issues. We have participated in the CoSQL shared task organized in the IntEx-SemPar workshop at EMNLP 2020. We have trained a number of Neural Machine Translation (NMT) models to efficiently generate the natural language responses from SQL. Our shuffled back-translation model has led to a BLEU score of 7.47 on the unknown test dataset. In this paper, we will discuss our methodologies to approach the problem and future directions to improve the quality of the generated natural language responses.</abstract>
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%0 Conference Proceedings
%T Natural Language Response Generation from SQL with Generalization and Back-translation
%A Bandyopadhyay, Saptarashmi
%A Zhao, Tianyang
%Y Bogin, Ben
%Y Iyer, Srinivasan
%Y Lin, Xi Victoria
%Y Radev, Dragomir
%Y Suhr, Alane
%Y Xiong, Caiming
%Y Yin, Pengcheng
%Y Yu, Tao
%Y Zhang, Rui
%Y Zhong, Victor
%E Panupong
%S Proceedings of the First Workshop on Interactive and Executable Semantic Parsing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F bandyopadhyay-zhao-2020-natural
%X Generation of natural language responses to the queries of structured language like SQL is very challenging as it requires generalization to new domains and the ability to answer ambiguous queries among other issues. We have participated in the CoSQL shared task organized in the IntEx-SemPar workshop at EMNLP 2020. We have trained a number of Neural Machine Translation (NMT) models to efficiently generate the natural language responses from SQL. Our shuffled back-translation model has led to a BLEU score of 7.47 on the unknown test dataset. In this paper, we will discuss our methodologies to approach the problem and future directions to improve the quality of the generated natural language responses.
%R 10.18653/v1/2020.intexsempar-1.6
%U https://aclanthology.org/2020.intexsempar-1.6
%U https://doi.org/10.18653/v1/2020.intexsempar-1.6
%P 46-49
Markdown (Informal)
[Natural Language Response Generation from SQL with Generalization and Back-translation](https://aclanthology.org/2020.intexsempar-1.6) (Bandyopadhyay & Zhao, intexsempar 2020)
ACL