<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zeng-etal-2023-bit">
    <titleInfo>
        <title>BIT-ACT: An Ancient Chinese Translation System Using Data Augmentation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Li</namePart>
        <namePart type="family">Zeng</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yanzhi</namePart>
        <namePart type="family">Tian</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yingyu</namePart>
        <namePart type="family">Shan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yuhang</namePart>
        <namePart type="family">Guo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of ALT2023: Ancient Language Translation Workshop</title>
        </titleInfo>
        <originInfo>
            <publisher>Asia-Pacific Association for Machine Translation</publisher>
            <place>
                <placeTerm type="text">Macau SAR, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper describes a translation model for ancient Chinese to modern Chinese and English for the Evahan 2023 competition, a subtask of the Ancient Language Translation 2023 challenge. During the training of our model, we applied various data augmentation techniques and used SiKu-RoBERTa as part of our model architecture. The results indicate that back translation improves the model’s performance, but double back translation introduces noise and harms the model’s performance. Fine-tuning on the original dataset can be helpful in solving the issue.</abstract>
    <identifier type="citekey">zeng-etal-2023-bit</identifier>
    <location>
        <url>https://aclanthology.org/2023.alt-1.6</url>
    </location>
    <part>
        <date>2023-09</date>
        <extent unit="page">
            <start>43</start>
            <end>47</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T BIT-ACT: An Ancient Chinese Translation System Using Data Augmentation
%A Zeng, Li
%A Tian, Yanzhi
%A Shan, Yingyu
%A Guo, Yuhang
%S Proceedings of ALT2023: Ancient Language Translation Workshop
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F zeng-etal-2023-bit
%X This paper describes a translation model for ancient Chinese to modern Chinese and English for the Evahan 2023 competition, a subtask of the Ancient Language Translation 2023 challenge. During the training of our model, we applied various data augmentation techniques and used SiKu-RoBERTa as part of our model architecture. The results indicate that back translation improves the model’s performance, but double back translation introduces noise and harms the model’s performance. Fine-tuning on the original dataset can be helpful in solving the issue.
%U https://aclanthology.org/2023.alt-1.6
%P 43-47

Markdown (Informal)

[BIT-ACT: An Ancient Chinese Translation System Using Data Augmentation](https://aclanthology.org/2023.alt-1.6) (Zeng et al., alt 2023)

• BIT-ACT: An Ancient Chinese Translation System Using Data Augmentation (Zeng et al., alt 2023)

ACL

• Li Zeng, Yanzhi Tian, Yingyu Shan, and Yuhang Guo. 2023. BIT-ACT: An Ancient Chinese Translation System Using Data Augmentation. In Proceedings of ALT2023: Ancient Language Translation Workshop, pages 43–47, Macau SAR, China. Asia-Pacific Association for Machine Translation.