<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2019-semi-supervised">
    <titleInfo>
        <title>Semi-supervised Stochastic Multi-Domain Learning using Variational Inference</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yitong</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Timothy</namePart>
        <namePart type="family">Baldwin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Trevor</namePart>
        <namePart type="family">Cohn</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Traum</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Supervised models of NLP rely on large collections of text which closely resemble the intended testing setting. Unfortunately matching text is often not available in sufficient quantity, and moreover, within any domain of text, data is often highly heterogenous. In this paper we propose a method to distill the important domain signal as part of a multi-domain learning system, using a latent variable model in which parts of a neural model are stochastically gated based on the inferred domain. We compare the use of discrete versus continuous latent variables, operating in a domain-supervised or a domain semi-supervised setting, where the domain is known only for a subset of training inputs. We show that our model leads to substantial performance improvements over competitive benchmark domain adaptation methods, including methods using adversarial learning.</abstract>
    <identifier type="citekey">li-etal-2019-semi-supervised</identifier>
    <identifier type="doi">10.18653/v1/P19-1186</identifier>
    <location>
        <url>https://aclanthology.org/P19-1186</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>1923</start>
            <end>1934</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Semi-supervised Stochastic Multi-Domain Learning using Variational Inference
%A Li, Yitong
%A Baldwin, Timothy
%A Cohn, Trevor
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F li-etal-2019-semi-supervised
%X Supervised models of NLP rely on large collections of text which closely resemble the intended testing setting. Unfortunately matching text is often not available in sufficient quantity, and moreover, within any domain of text, data is often highly heterogenous. In this paper we propose a method to distill the important domain signal as part of a multi-domain learning system, using a latent variable model in which parts of a neural model are stochastically gated based on the inferred domain. We compare the use of discrete versus continuous latent variables, operating in a domain-supervised or a domain semi-supervised setting, where the domain is known only for a subset of training inputs. We show that our model leads to substantial performance improvements over competitive benchmark domain adaptation methods, including methods using adversarial learning.
%R 10.18653/v1/P19-1186
%U https://aclanthology.org/P19-1186
%U https://doi.org/10.18653/v1/P19-1186
%P 1923-1934

Markdown (Informal)

[Semi-supervised Stochastic Multi-Domain Learning using Variational Inference](https://aclanthology.org/P19-1186) (Li et al., ACL 2019)

• Semi-supervised Stochastic Multi-Domain Learning using Variational Inference (Li et al., ACL 2019)

ACL

• Yitong Li, Timothy Baldwin, and Trevor Cohn. 2019. Semi-supervised Stochastic Multi-Domain Learning using Variational Inference. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1923–1934, Florence, Italy. Association for Computational Linguistics.