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    <titleInfo>
        <title>Universal Discourse Representation Structure Parsing</title>
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    <name type="personal">
        <namePart type="given">Jiangming</namePart>
        <namePart type="family">Liu</namePart>
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        <namePart type="given">Shay</namePart>
        <namePart type="given">B</namePart>
        <namePart type="family">Cohen</namePart>
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    <name type="personal">
        <namePart type="given">Mirella</namePart>
        <namePart type="family">Lapata</namePart>
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    <name type="personal">
        <namePart type="given">Johan</namePart>
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    <abstract>We consider the task of crosslingual semantic parsing in the style of Discourse Representation Theory (DRT) where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide learning in other languages. We introduce \ensuremath\mathbbUniversal Discourse Representation Theory (\ensuremath\mathbbUDRT), a variant of DRT that explicitly anchors semantic representations to tokens in the linguistic input. We develop a semantic parsing framework based on the Transformer architecture and utilize it to obtain semantic resources in multiple languages following two learning schemes. The many-to-one approach translates non-English text to English, and then runs a relatively accurate English parser on the translated text, while the one-to-many approach translates gold standard English to non-English text and trains multiple parsers (one per language) on the translations. Experimental results on the Parallel Meaning Bank show that our proposal outperforms strong baselines by a wide margin and can be used to construct (silver-standard) meaning banks for 99 languages.</abstract>
    <identifier type="citekey">liu-etal-2021-universal</identifier>
    <identifier type="doi">10.1162/coli_a_00406</identifier>
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        <url>https://aclanthology.org/2021.cl-2.15/</url>
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        <date>2021-06</date>
        <detail type="volume"><number>47</number></detail>
        <detail type="issue"><number>2</number></detail>
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            <start>445</start>
            <end>476</end>
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%0 Journal Article
%T Universal Discourse Representation Structure Parsing
%A Liu, Jiangming
%A Cohen, Shay B.
%A Lapata, Mirella
%A Bos, Johan
%J Computational Linguistics
%D 2021
%8 June
%V 47
%N 2
%I MIT Press
%C Cambridge, MA
%F liu-etal-2021-universal
%X We consider the task of crosslingual semantic parsing in the style of Discourse Representation Theory (DRT) where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide learning in other languages. We introduce \ensuremath\mathbbUniversal Discourse Representation Theory (\ensuremath\mathbbUDRT), a variant of DRT that explicitly anchors semantic representations to tokens in the linguistic input. We develop a semantic parsing framework based on the Transformer architecture and utilize it to obtain semantic resources in multiple languages following two learning schemes. The many-to-one approach translates non-English text to English, and then runs a relatively accurate English parser on the translated text, while the one-to-many approach translates gold standard English to non-English text and trains multiple parsers (one per language) on the translations. Experimental results on the Parallel Meaning Bank show that our proposal outperforms strong baselines by a wide margin and can be used to construct (silver-standard) meaning banks for 99 languages.
%R 10.1162/coli_a_00406
%U https://aclanthology.org/2021.cl-2.15/
%U https://doi.org/10.1162/coli_a_00406
%P 445-476

Markdown (Informal)

[Universal Discourse Representation Structure Parsing](https://aclanthology.org/2021.cl-2.15/) (Liu et al., CL 2021)

• Universal Discourse Representation Structure Parsing (Liu et al., CL 2021)

ACL

• Jiangming Liu, Shay B. Cohen, Mirella Lapata, and Johan Bos. 2021. Universal Discourse Representation Structure Parsing. Computational Linguistics, 47(2):445–476.