<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tang-etal-2020-understanding">
    <titleInfo>
        <title>Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Gongbo</namePart>
        <namePart type="family">Tang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rico</namePart>
        <namePart type="family">Sennrich</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Joakim</namePart>
        <namePart type="family">Nivre</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>
        <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>Recent work has shown that deeper character-based neural machine translation (NMT) models can outperform subword-based models. However, it is still unclear what makes deeper character-based models successful. In this paper, we conduct an investigation into pure character-based models in the case of translating Finnish into English, including exploring the ability to learn word senses and morphological inflections and the attention mechanism. We demonstrate that word-level information is distributed over the entire character sequence rather than over a single character, and characters at different positions play different roles in learning linguistic knowledge. In addition, character-based models need more layers to encode word senses which explains why only deeper models outperform subword-based models. The attention distribution pattern shows that separators attract a lot of attention and we explore a sparse word-level attention to enforce character hidden states to capture the full word-level information. Experimental results show that the word-level attention with a single head results in 1.2 BLEU points drop.</abstract>
    <identifier type="citekey">tang-etal-2020-understanding</identifier>
    <identifier type="doi">10.18653/v1/2020.coling-main.375</identifier>
    <location>
        <url>https://aclanthology.org/2020.coling-main.375</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>4251</start>
            <end>4262</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English
%A Tang, Gongbo
%A Sennrich, Rico
%A Nivre, Joakim
%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 tang-etal-2020-understanding
%X Recent work has shown that deeper character-based neural machine translation (NMT) models can outperform subword-based models. However, it is still unclear what makes deeper character-based models successful. In this paper, we conduct an investigation into pure character-based models in the case of translating Finnish into English, including exploring the ability to learn word senses and morphological inflections and the attention mechanism. We demonstrate that word-level information is distributed over the entire character sequence rather than over a single character, and characters at different positions play different roles in learning linguistic knowledge. In addition, character-based models need more layers to encode word senses which explains why only deeper models outperform subword-based models. The attention distribution pattern shows that separators attract a lot of attention and we explore a sparse word-level attention to enforce character hidden states to capture the full word-level information. Experimental results show that the word-level attention with a single head results in 1.2 BLEU points drop.
%R 10.18653/v1/2020.coling-main.375
%U https://aclanthology.org/2020.coling-main.375
%U https://doi.org/10.18653/v1/2020.coling-main.375
%P 4251-4262

Markdown (Informal)

[Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English](https://aclanthology.org/2020.coling-main.375) (Tang et al., COLING 2020)

• Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English (Tang et al., COLING 2020)

ACL

• Gongbo Tang, Rico Sennrich, and Joakim Nivre. 2020. Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4251–4262, Barcelona, Spain (Online). International Committee on Computational Linguistics.