<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="liu-etal-2020-learning-distributed">
    <titleInfo>
        <title>Learning distributed sentence vectors with bi-directional 3D convolutions</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Bin</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Liang</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Guosheng</namePart>
        <namePart type="family">Yin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chengqing</namePart>
            <namePart type="family">Zong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>International Committee on Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Barcelona, Spain (Online)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose to learn distributed sentence representation using text’s visual features as input. Different from the existing methods that render the words or characters of a sentence into images separately, we further fold these images into a 3-dimensional sentence tensor. Then, multiple 3-dimensional convolutions with different lengths (the third dimension) are applied to the sentence tensor, which act as bi-gram, tri-gram, quad-gram, and even five-gram detectors jointly. Similar to the Bi-LSTM, these n-gram detectors learn both forward and backward distributional semantic knowledge from the sentence tensor. That is, the proposed model using bi-directional convolutions to learn text embedding according to the semantic order of words. The feature maps from the two directions are concatenated for final sentence embedding learning. Our model involves only a single-layer of convolution which makes it easy and fast to train. Finally, we evaluate the sentence embeddings on several downstream Natural Language Processing (NLP) tasks, which demonstrate a surprisingly excellent performance of the proposed model.</abstract>
    <identifier type="citekey">liu-etal-2020-learning-distributed</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.601</identifier>
    <location>
        <url>https://aclanthology.org/2020.coling-main.601</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>6820</start>
            <end>6830</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Learning distributed sentence vectors with bi-directional 3D convolutions
%A Liu, Bin
%A Wang, Liang
%A Yin, Guosheng
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F liu-etal-2020-learning-distributed
%X We propose to learn distributed sentence representation using text’s visual features as input. Different from the existing methods that render the words or characters of a sentence into images separately, we further fold these images into a 3-dimensional sentence tensor. Then, multiple 3-dimensional convolutions with different lengths (the third dimension) are applied to the sentence tensor, which act as bi-gram, tri-gram, quad-gram, and even five-gram detectors jointly. Similar to the Bi-LSTM, these n-gram detectors learn both forward and backward distributional semantic knowledge from the sentence tensor. That is, the proposed model using bi-directional convolutions to learn text embedding according to the semantic order of words. The feature maps from the two directions are concatenated for final sentence embedding learning. Our model involves only a single-layer of convolution which makes it easy and fast to train. Finally, we evaluate the sentence embeddings on several downstream Natural Language Processing (NLP) tasks, which demonstrate a surprisingly excellent performance of the proposed model.
%R 10.18653/v1/2020.coling-main.601
%U https://aclanthology.org/2020.coling-main.601
%U https://doi.org/10.18653/v1/2020.coling-main.601
%P 6820-6830

Markdown (Informal)

[Learning distributed sentence vectors with bi-directional 3D convolutions](https://aclanthology.org/2020.coling-main.601) (Liu et al., COLING 2020)

• Learning distributed sentence vectors with bi-directional 3D convolutions (Liu et al., COLING 2020)

ACL

• Bin Liu, Liang Wang, and Guosheng Yin. 2020. Learning distributed sentence vectors with bi-directional 3D convolutions. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6820–6830, Barcelona, Spain (Online). International Committee on Computational Linguistics.