Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing

Jean Maillard; Stephen Clark

doi:10.18653/v1/W18-2903

Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing

Correct Metadata for

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines which use (externally provided) syntax trees to drive the composition order. This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model.

Anthology ID:: W18-2903
Volume:: Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP
Month:: July
Year:: 2018
Address:: Melbourne, Australia
Editors:: Georgiana Dinu, Miguel Ballesteros, Avirup Sil, Sam Bowman, Wael Hamza, Anders Sogaard, Tahira Naseem, Yoav Goldberg
Venue:: ACL
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 13–18
Language:
URL:: https://aclanthology.org/W18-2903/
DOI:: 10.18653/v1/W18-2903
Bibkey:
Cite (ACL):: Jean Maillard and Stephen Clark. 2018. Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing. In Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP, pages 13–18, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):: Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing (Maillard & Clark, ACL 2018)
Copy Citation:
PDF:: https://aclanthology.org/W18-2903.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{maillard-clark-2018-latent,
    title = "Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing",
    author = "Maillard, Jean  and
      Clark, Stephen",
    editor = "Dinu, Georgiana  and
      Ballesteros, Miguel  and
      Sil, Avirup  and
      Bowman, Sam  and
      Hamza, Wael  and
      Sogaard, Anders  and
      Naseem, Tahira  and
      Goldberg, Yoav",
    booktitle = "Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for {NLP}",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-2903/",
    doi = "10.18653/v1/W18-2903",
    pages = "13--18",
    abstract = "Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines which use (externally provided) syntax trees to drive the composition order. This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="maillard-clark-2018-latent">
    <titleInfo>
        <title>Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jean</namePart>
        <namePart type="family">Maillard</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Stephen</namePart>
        <namePart type="family">Clark</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Georgiana</namePart>
            <namePart type="family">Dinu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Miguel</namePart>
            <namePart type="family">Ballesteros</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Avirup</namePart>
            <namePart type="family">Sil</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sam</namePart>
            <namePart type="family">Bowman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Wael</namePart>
            <namePart type="family">Hamza</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anders</namePart>
            <namePart type="family">Sogaard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tahira</namePart>
            <namePart type="family">Naseem</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines which use (externally provided) syntax trees to drive the composition order. This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model.</abstract>
    <identifier type="citekey">maillard-clark-2018-latent</identifier>
    <identifier type="doi">10.18653/v1/W18-2903</identifier>
    <location>
        <url>https://aclanthology.org/W18-2903/</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>13</start>
            <end>18</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing
%A Maillard, Jean
%A Clark, Stephen
%Y Dinu, Georgiana
%Y Ballesteros, Miguel
%Y Sil, Avirup
%Y Bowman, Sam
%Y Hamza, Wael
%Y Sogaard, Anders
%Y Naseem, Tahira
%Y Goldberg, Yoav
%S Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F maillard-clark-2018-latent
%X Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines which use (externally provided) syntax trees to drive the composition order. This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model.
%R 10.18653/v1/W18-2903
%U https://aclanthology.org/W18-2903/
%U https://doi.org/10.18653/v1/W18-2903
%P 13-18

Download as File

Markdown (Informal)

[Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing](https://aclanthology.org/W18-2903/) (Maillard & Clark, ACL 2018)

Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing (Maillard & Clark, ACL 2018)

ACL

Jean Maillard and Stephen Clark. 2018. Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing. In Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP, pages 13–18, Melbourne, Australia. Association for Computational Linguistics.