<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhou-etal-2022-hierarchical">
    <titleInfo>
        <title>Hierarchical Recurrent Aggregative Generation for Few-Shot NLG</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Giulio</namePart>
        <namePart type="family">Zhou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gerasimos</namePart>
        <namePart type="family">Lampouras</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ignacio</namePart>
        <namePart type="family">Iacobacci</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: ACL 2022</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aline</namePart>
            <namePart type="family">Villavicencio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dublin, Ireland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Large pretrained models enable transfer learning to low-resource domains for language generation tasks. However, previous end-to-end approaches do not account for the fact that some generation sub-tasks, specifically aggregation and lexicalisation, can benefit from transfer learning in different extents. To exploit these varying potentials for transfer learning, we propose a new hierarchical approach for few-shot and zero-shot generation. Our approach consists of a three-moduled jointly trained architecture: the first module independently lexicalises the distinct units of information in the input as sentence sub-units (e.g. phrases), the second module recurrently aggregates these sub-units to generate a unified intermediate output, while the third module subsequently post-edits it to generate a coherent and fluent final text. We perform extensive empirical analysis and ablation studies on few-shot and zero-shot settings across 4 datasets. Automatic and human evaluation shows that the proposed hierarchical approach is consistently capable of achieving state-of-the-art results when compared to previous work.</abstract>
    <identifier type="citekey">zhou-etal-2022-hierarchical</identifier>
    <identifier type="doi">10.18653/v1/2022.findings-acl.170</identifier>
    <location>
        <url>https://aclanthology.org/2022.findings-acl.170</url>
    </location>
    <part>
        <date>2022-05</date>
        <extent unit="page">
            <start>2167</start>
            <end>2181</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Hierarchical Recurrent Aggregative Generation for Few-Shot NLG
%A Zhou, Giulio
%A Lampouras, Gerasimos
%A Iacobacci, Ignacio
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhou-etal-2022-hierarchical
%X Large pretrained models enable transfer learning to low-resource domains for language generation tasks. However, previous end-to-end approaches do not account for the fact that some generation sub-tasks, specifically aggregation and lexicalisation, can benefit from transfer learning in different extents. To exploit these varying potentials for transfer learning, we propose a new hierarchical approach for few-shot and zero-shot generation. Our approach consists of a three-moduled jointly trained architecture: the first module independently lexicalises the distinct units of information in the input as sentence sub-units (e.g. phrases), the second module recurrently aggregates these sub-units to generate a unified intermediate output, while the third module subsequently post-edits it to generate a coherent and fluent final text. We perform extensive empirical analysis and ablation studies on few-shot and zero-shot settings across 4 datasets. Automatic and human evaluation shows that the proposed hierarchical approach is consistently capable of achieving state-of-the-art results when compared to previous work.
%R 10.18653/v1/2022.findings-acl.170
%U https://aclanthology.org/2022.findings-acl.170
%U https://doi.org/10.18653/v1/2022.findings-acl.170
%P 2167-2181

Markdown (Informal)

[Hierarchical Recurrent Aggregative Generation for Few-Shot NLG](https://aclanthology.org/2022.findings-acl.170) (Zhou et al., Findings 2022)

• Hierarchical Recurrent Aggregative Generation for Few-Shot NLG (Zhou et al., Findings 2022)

ACL

• Giulio Zhou, Gerasimos Lampouras, and Ignacio Iacobacci. 2022. Hierarchical Recurrent Aggregative Generation for Few-Shot NLG. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2167–2181, Dublin, Ireland. Association for Computational Linguistics.