<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ahamad-2019-generating">
    <titleInfo>
        <title>Generating Text through Adversarial Training Using Skip-Thought Vectors</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Afroz</namePart>
        <namePart type="family">Ahamad</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Sudipta</namePart>
            <namePart type="family">Kar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Farah</namePart>
            <namePart type="family">Nadeem</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Laura</namePart>
            <namePart type="family">Burdick</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Greg</namePart>
            <namePart type="family">Durrett</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Na-Rae</namePart>
            <namePart type="family">Han</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Minneapolis, Minnesota</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>GANs have been shown to perform exceedingly well on tasks pertaining to image generation and style transfer. In the field of language modelling, word embeddings such as GLoVe and word2vec are state-of-the-art methods for applying neural network models on textual data. Attempts have been made to utilize GANs with word embeddings for text generation. This study presents an approach to text generation using Skip-Thought sentence embeddings with GANs based on gradient penalty functions and f-measures. The proposed architecture aims to reproduce writing style in the generated text by modelling the way of expression at a sentence level across all the works of an author. Extensive experiments were run in different embedding settings on a variety of tasks including conditional text generation and language generation. The model outperforms baseline text generation networks across several automated evaluation metrics like BLEU-n, METEOR and ROUGE. Further, wide applicability and effectiveness in real life tasks are demonstrated through human judgement scores.</abstract>
    <identifier type="citekey">ahamad-2019-generating</identifier>
    <identifier type="doi">10.18653/v1/N19-3008</identifier>
    <location>
        <url>https://aclanthology.org/N19-3008</url>
    </location>
    <part>
        <date>2019-06</date>
        <extent unit="page">
            <start>53</start>
            <end>60</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Generating Text through Adversarial Training Using Skip-Thought Vectors
%A Ahamad, Afroz
%Y Kar, Sudipta
%Y Nadeem, Farah
%Y Burdick, Laura
%Y Durrett, Greg
%Y Han, Na-Rae
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F ahamad-2019-generating
%X GANs have been shown to perform exceedingly well on tasks pertaining to image generation and style transfer. In the field of language modelling, word embeddings such as GLoVe and word2vec are state-of-the-art methods for applying neural network models on textual data. Attempts have been made to utilize GANs with word embeddings for text generation. This study presents an approach to text generation using Skip-Thought sentence embeddings with GANs based on gradient penalty functions and f-measures. The proposed architecture aims to reproduce writing style in the generated text by modelling the way of expression at a sentence level across all the works of an author. Extensive experiments were run in different embedding settings on a variety of tasks including conditional text generation and language generation. The model outperforms baseline text generation networks across several automated evaluation metrics like BLEU-n, METEOR and ROUGE. Further, wide applicability and effectiveness in real life tasks are demonstrated through human judgement scores.
%R 10.18653/v1/N19-3008
%U https://aclanthology.org/N19-3008
%U https://doi.org/10.18653/v1/N19-3008
%P 53-60

Markdown (Informal)

[Generating Text through Adversarial Training Using Skip-Thought Vectors](https://aclanthology.org/N19-3008) (Ahamad, NAACL 2019)

• Generating Text through Adversarial Training Using Skip-Thought Vectors (Ahamad, NAACL 2019)

ACL

• Afroz Ahamad. 2019. Generating Text through Adversarial Training Using Skip-Thought Vectors. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 53–60, Minneapolis, Minnesota. Association for Computational Linguistics.