@inproceedings{lebanoff-etal-2021-semantic,
title = "Semantic Parsing of Brief and Multi-Intent Natural Language Utterances",
author = "Lebanoff, Logan and
Newton, Charles and
Hung, Victor and
Atkinson, Beth and
Killilea, John and
Liu, Fei",
editor = "Ben-David, Eyal and
Cohen, Shay and
McDonald, Ryan and
Plank, Barbara and
Reichart, Roi and
Rotman, Guy and
Ziser, Yftah",
booktitle = "Proceedings of the Second Workshop on Domain Adaptation for NLP",
month = apr,
year = "2021",
address = "Kyiv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.adaptnlp-1.25",
pages = "255--262",
abstract = "Many military communication domains involve rapidly conveying situation awareness with few words. Converting natural language utterances to logical forms in these domains is challenging, as these utterances are brief and contain multiple intents. In this paper, we present a first effort toward building a weakly-supervised semantic parser to transform brief, multi-intent natural utterances into logical forms. Our findings suggest a new {``}projection and reduction{''} method that iteratively performs projection from natural to canonical utterances followed by reduction of natural utterances is the most effective. We conduct extensive experiments on two military and a general-domain dataset and provide a new baseline for future research toward accurate parsing of multi-intent utterances.",
}
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%0 Conference Proceedings
%T Semantic Parsing of Brief and Multi-Intent Natural Language Utterances
%A Lebanoff, Logan
%A Newton, Charles
%A Hung, Victor
%A Atkinson, Beth
%A Killilea, John
%A Liu, Fei
%Y Ben-David, Eyal
%Y Cohen, Shay
%Y McDonald, Ryan
%Y Plank, Barbara
%Y Reichart, Roi
%Y Rotman, Guy
%Y Ziser, Yftah
%S Proceedings of the Second Workshop on Domain Adaptation for NLP
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv, Ukraine
%F lebanoff-etal-2021-semantic
%X Many military communication domains involve rapidly conveying situation awareness with few words. Converting natural language utterances to logical forms in these domains is challenging, as these utterances are brief and contain multiple intents. In this paper, we present a first effort toward building a weakly-supervised semantic parser to transform brief, multi-intent natural utterances into logical forms. Our findings suggest a new “projection and reduction” method that iteratively performs projection from natural to canonical utterances followed by reduction of natural utterances is the most effective. We conduct extensive experiments on two military and a general-domain dataset and provide a new baseline for future research toward accurate parsing of multi-intent utterances.
%U https://aclanthology.org/2021.adaptnlp-1.25
%P 255-262
Markdown (Informal)
[Semantic Parsing of Brief and Multi-Intent Natural Language Utterances](https://aclanthology.org/2021.adaptnlp-1.25) (Lebanoff et al., AdaptNLP 2021)
ACL