@inproceedings{bharadwaj-shevade-2022-efficient,
title = "Efficient Constituency Tree based Encoding for Natural Language to Bash Translation",
author = "Bharadwaj, Shikhar and
Shevade, Shirish",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.230",
doi = "10.18653/v1/2022.naacl-main.230",
pages = "3159--3168",
abstract = "Bash is a Unix command language used for interacting with the Operating System. Recent works on natural language to Bash translation have made significant advances, but none of the previous methods utilize the problem{'}s inherent structure. We identify this structure andpropose a Segmented Invocation Transformer (SIT) that utilizes the information from the constituency parse tree of the natural language text. Our method is motivated by the alignment between segments in the natural language text and Bash command components. Incorporating the structure in the modelling improves the performance of the model. Since such systems must be universally accessible, we benchmark the inference times on a CPU rather than a GPU. We observe a 1.8x improvement in the inference time and a 5x reduction in model parameters. Attribution analysis using Integrated Gradients reveals that the proposed method can capture the problem structure.",
}
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%0 Conference Proceedings
%T Efficient Constituency Tree based Encoding for Natural Language to Bash Translation
%A Bharadwaj, Shikhar
%A Shevade, Shirish
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F bharadwaj-shevade-2022-efficient
%X Bash is a Unix command language used for interacting with the Operating System. Recent works on natural language to Bash translation have made significant advances, but none of the previous methods utilize the problem’s inherent structure. We identify this structure andpropose a Segmented Invocation Transformer (SIT) that utilizes the information from the constituency parse tree of the natural language text. Our method is motivated by the alignment between segments in the natural language text and Bash command components. Incorporating the structure in the modelling improves the performance of the model. Since such systems must be universally accessible, we benchmark the inference times on a CPU rather than a GPU. We observe a 1.8x improvement in the inference time and a 5x reduction in model parameters. Attribution analysis using Integrated Gradients reveals that the proposed method can capture the problem structure.
%R 10.18653/v1/2022.naacl-main.230
%U https://aclanthology.org/2022.naacl-main.230
%U https://doi.org/10.18653/v1/2022.naacl-main.230
%P 3159-3168
Markdown (Informal)
[Efficient Constituency Tree based Encoding for Natural Language to Bash Translation](https://aclanthology.org/2022.naacl-main.230) (Bharadwaj & Shevade, NAACL 2022)
ACL