<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pu-simaan-2022-passing">
    <titleInfo>
        <title>Passing Parser Uncertainty to the Transformer: Labeled Dependency Distributions for Neural Machine Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Dongqi</namePart>
        <namePart type="family">Pu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Khalil</namePart>
        <namePart type="family">Sima’an</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 23rd Annual Conference of the European Association for Machine Translation</title>
        </titleInfo>
        <originInfo>
            <publisher>European Association for Machine Translation</publisher>
            <place>
                <placeTerm type="text">Ghent, Belgium</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Existing syntax-enriched neural machine translation (NMT) models work either with the single most-likely unlabeled parse or the set of n-best unlabeled parses coming out of an external parser. Passing a single or n-best parses to the NMT model risks propagating parse errors. Furthermore, unlabeled parses represent only syntactic groupings without their linguistically relevant categories. In this paper we explore the question: Does passing both parser uncertainty and labeled syntactic knowledge to the Transformer improve its translation performance? This paper contributes a novel method for infusing the whole labeled dependency distributions (LDD) of the source sentence’s dependency forest into the self-attention mechanism of the encoder of the Transformer. A range of experimental results on three language pairs demonstrate that the proposed approach outperforms both the vanilla Transformer as well as the single best-parse Transformer model across several evaluation metrics.</abstract>
    <identifier type="citekey">pu-simaan-2022-passing</identifier>
    <location>
        <url>https://aclanthology.org/2022.eamt-1.7</url>
    </location>
    <part>
        <date>2022-06</date>
        <extent unit="page">
            <start>41</start>
            <end>50</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Passing Parser Uncertainty to the Transformer: Labeled Dependency Distributions for Neural Machine Translation
%A Pu, Dongqi
%A Sima’an, Khalil
%S Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
%D 2022
%8 June
%I European Association for Machine Translation
%C Ghent, Belgium
%F pu-simaan-2022-passing
%X Existing syntax-enriched neural machine translation (NMT) models work either with the single most-likely unlabeled parse or the set of n-best unlabeled parses coming out of an external parser. Passing a single or n-best parses to the NMT model risks propagating parse errors. Furthermore, unlabeled parses represent only syntactic groupings without their linguistically relevant categories. In this paper we explore the question: Does passing both parser uncertainty and labeled syntactic knowledge to the Transformer improve its translation performance? This paper contributes a novel method for infusing the whole labeled dependency distributions (LDD) of the source sentence’s dependency forest into the self-attention mechanism of the encoder of the Transformer. A range of experimental results on three language pairs demonstrate that the proposed approach outperforms both the vanilla Transformer as well as the single best-parse Transformer model across several evaluation metrics.
%U https://aclanthology.org/2022.eamt-1.7
%P 41-50

Markdown (Informal)

[Passing Parser Uncertainty to the Transformer: Labeled Dependency Distributions for Neural Machine Translation](https://aclanthology.org/2022.eamt-1.7) (Pu & Sima’an, EAMT 2022)

• Passing Parser Uncertainty to the Transformer: Labeled Dependency Distributions for Neural Machine Translation (Pu & Sima’an, EAMT 2022)

ACL

• Dongqi Pu and Khalil Sima’an. 2022. Passing Parser Uncertainty to the Transformer: Labeled Dependency Distributions for Neural Machine Translation. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 41–50, Ghent, Belgium. European Association for Machine Translation.