@inproceedings{sen-groves-2021-semantic,
title = "Semantic Parsing of Disfluent Speech",
author = "Sen, Priyanka and
Groves, Isabel",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.150",
doi = "10.18653/v1/2021.eacl-main.150",
pages = "1748--1753",
abstract = "Speech disfluencies are prevalent in spontaneous speech. The rising popularity of voice assistants presents a growing need to handle naturally occurring disfluencies. Semantic parsing is a key component for understanding user utterances in voice assistants, yet most semantic parsing research to date focuses on written text. In this paper, we investigate semantic parsing of disfluent speech with the ATIS dataset. We find that a state-of-the-art semantic parser does not seamlessly handle disfluencies. We experiment with adding real and synthetic disfluencies at training time and find that adding synthetic disfluencies not only improves model performance by up to 39{\%} but can also outperform adding real disfluencies in the ATIS dataset.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sen-groves-2021-semantic">
<titleInfo>
<title>Semantic Parsing of Disfluent Speech</title>
</titleInfo>
<name type="personal">
<namePart type="given">Priyanka</namePart>
<namePart type="family">Sen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Isabel</namePart>
<namePart type="family">Groves</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume</title>
</titleInfo>
<name type="personal">
<namePart type="given">Paola</namePart>
<namePart type="family">Merlo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jorg</namePart>
<namePart type="family">Tiedemann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Reut</namePart>
<namePart type="family">Tsarfaty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Speech disfluencies are prevalent in spontaneous speech. The rising popularity of voice assistants presents a growing need to handle naturally occurring disfluencies. Semantic parsing is a key component for understanding user utterances in voice assistants, yet most semantic parsing research to date focuses on written text. In this paper, we investigate semantic parsing of disfluent speech with the ATIS dataset. We find that a state-of-the-art semantic parser does not seamlessly handle disfluencies. We experiment with adding real and synthetic disfluencies at training time and find that adding synthetic disfluencies not only improves model performance by up to 39% but can also outperform adding real disfluencies in the ATIS dataset.</abstract>
<identifier type="citekey">sen-groves-2021-semantic</identifier>
<identifier type="doi">10.18653/v1/2021.eacl-main.150</identifier>
<location>
<url>https://aclanthology.org/2021.eacl-main.150</url>
</location>
<part>
<date>2021-04</date>
<extent unit="page">
<start>1748</start>
<end>1753</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Semantic Parsing of Disfluent Speech
%A Sen, Priyanka
%A Groves, Isabel
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F sen-groves-2021-semantic
%X Speech disfluencies are prevalent in spontaneous speech. The rising popularity of voice assistants presents a growing need to handle naturally occurring disfluencies. Semantic parsing is a key component for understanding user utterances in voice assistants, yet most semantic parsing research to date focuses on written text. In this paper, we investigate semantic parsing of disfluent speech with the ATIS dataset. We find that a state-of-the-art semantic parser does not seamlessly handle disfluencies. We experiment with adding real and synthetic disfluencies at training time and find that adding synthetic disfluencies not only improves model performance by up to 39% but can also outperform adding real disfluencies in the ATIS dataset.
%R 10.18653/v1/2021.eacl-main.150
%U https://aclanthology.org/2021.eacl-main.150
%U https://doi.org/10.18653/v1/2021.eacl-main.150
%P 1748-1753
Markdown (Informal)
[Semantic Parsing of Disfluent Speech](https://aclanthology.org/2021.eacl-main.150) (Sen & Groves, EACL 2021)
ACL
- Priyanka Sen and Isabel Groves. 2021. Semantic Parsing of Disfluent Speech. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1748–1753, Online. Association for Computational Linguistics.