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        <title>Leveraging Non-Specialists for Accurate and Time Efficient AMR Annotation</title>
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    <name type="personal">
        <namePart type="given">Cecilia</namePart>
        <namePart type="family">Mauceri</namePart>
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    <name type="personal">
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            <title>Proceedings of the LREC 2020 Workshop on “Citizen Linguistics in Language Resource Development”</title>
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            <namePart type="given">Mark</namePart>
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            <namePart type="given">Chris</namePart>
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    <abstract>Abstract Meaning Representations (AMRs), a syntax-free representation of phrase semantics are useful for capturing the meaning of a phrase and reflecting the relationship between concepts that are referred to. However, annotating AMRs are time consuming and expensive. The existing annotation process requires expertly trained workers who have knowledge of an extensive set of guidelines for parsing phrases. In this paper, we propose a cost-saving two-step process for the creation of a corpus of AMR-phrase pairs for spatial referring expressions. The first step uses non-specialists to perform simple annotations that can be leveraged in the second step to accelerate the annotation performed by the experts. We hypothesize that our process will decrease the cost per annotation and improve consistency across annotators. Few corpora of spatial referring expressions exist and the resulting language resource will be valuable for referring expression comprehension and generation modeling.</abstract>
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%0 Conference Proceedings
%T Leveraging Non-Specialists for Accurate and Time Efficient AMR Annotation
%A Martin, Mary
%A Mauceri, Cecilia
%A Palmer, Martha
%A Heckman, Christoffer
%Y Fiumara, James
%Y Cieri, Christopher
%Y Liberman, Mark
%Y Callison-Burch, Chris
%S Proceedings of the LREC 2020 Workshop on “Citizen Linguistics in Language Resource Development”
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-59-7
%G English
%F martin-etal-2020-leveraging
%X Abstract Meaning Representations (AMRs), a syntax-free representation of phrase semantics are useful for capturing the meaning of a phrase and reflecting the relationship between concepts that are referred to. However, annotating AMRs are time consuming and expensive. The existing annotation process requires expertly trained workers who have knowledge of an extensive set of guidelines for parsing phrases. In this paper, we propose a cost-saving two-step process for the creation of a corpus of AMR-phrase pairs for spatial referring expressions. The first step uses non-specialists to perform simple annotations that can be leveraged in the second step to accelerate the annotation performed by the experts. We hypothesize that our process will decrease the cost per annotation and improve consistency across annotators. Few corpora of spatial referring expressions exist and the resulting language resource will be valuable for referring expression comprehension and generation modeling.
%U https://aclanthology.org/2020.cllrd-1.5
%P 35-39

Markdown (Informal)

[Leveraging Non-Specialists for Accurate and Time Efficient AMR Annotation](https://aclanthology.org/2020.cllrd-1.5) (Martin et al., CLLRD 2020)

• Leveraging Non-Specialists for Accurate and Time Efficient AMR Annotation (Martin et al., CLLRD 2020)

ACL

• Mary Martin, Cecilia Mauceri, Martha Palmer, and Christoffer Heckman. 2020. Leveraging Non-Specialists for Accurate and Time Efficient AMR Annotation. In Proceedings of the LREC 2020 Workshop on "Citizen Linguistics in Language Resource Development", pages 35–39, Marseille, France. European Language Resources Association.