@inproceedings{givoli-reichart-2019-zero,
title = "Zero-Shot Semantic Parsing for Instructions",
author = "Givoli, Ofer and
Reichart, Roi",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1438",
doi = "10.18653/v1/P19-1438",
pages = "4454--4464",
abstract = "We consider a zero-shot semantic parsing task: parsing instructions into compositional logical forms, in domains that were not seen during training. We present a new dataset with 1,390 examples from 7 application domains (e.g. a calendar or a file manager), each example consisting of a triplet: (a) the application{'}s initial state, (b) an instruction, to be carried out in the context of that state, and (c) the state of the application after carrying out the instruction. We introduce a new training algorithm that aims to train a semantic parser on examples from a set of source domains, so that it can effectively parse instructions from an unknown target domain. We integrate our algorithm into the floating parser of Pasupat and Liang (2015), and further augment the parser with features and a logical form candidate filtering logic, to support zero-shot adaptation. Our experiments with various zero-shot adaptation setups demonstrate substantial performance gains over a non-adapted parser.",
}
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%0 Conference Proceedings
%T Zero-Shot Semantic Parsing for Instructions
%A Givoli, Ofer
%A Reichart, Roi
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F givoli-reichart-2019-zero
%X We consider a zero-shot semantic parsing task: parsing instructions into compositional logical forms, in domains that were not seen during training. We present a new dataset with 1,390 examples from 7 application domains (e.g. a calendar or a file manager), each example consisting of a triplet: (a) the application’s initial state, (b) an instruction, to be carried out in the context of that state, and (c) the state of the application after carrying out the instruction. We introduce a new training algorithm that aims to train a semantic parser on examples from a set of source domains, so that it can effectively parse instructions from an unknown target domain. We integrate our algorithm into the floating parser of Pasupat and Liang (2015), and further augment the parser with features and a logical form candidate filtering logic, to support zero-shot adaptation. Our experiments with various zero-shot adaptation setups demonstrate substantial performance gains over a non-adapted parser.
%R 10.18653/v1/P19-1438
%U https://aclanthology.org/P19-1438
%U https://doi.org/10.18653/v1/P19-1438
%P 4454-4464
Markdown (Informal)
[Zero-Shot Semantic Parsing for Instructions](https://aclanthology.org/P19-1438) (Givoli & Reichart, ACL 2019)
ACL
- Ofer Givoli and Roi Reichart. 2019. Zero-Shot Semantic Parsing for Instructions. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4454–4464, Florence, Italy. Association for Computational Linguistics.