@inproceedings{qingfu-etal-2024-spancs,
title = "{S}pan{CS}:面向跨语言代码生成的片段级语码转换({S}pan{CS}: Span-Level Code-Switching for Cross-Lingual Code Generation)",
author = "Zhu, Qingfu and
Zhou, Shiqi and
Wang, Shuo and
Zhang, Zhiming and
Wang, Haoyu and
Chen, Qiguang and
Che, Wanxiang",
editor = "Maosong, Sun and
Jiye, Liang and
Xianpei, Han and
Zhiyuan, Liu and
Yulan, He",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.6/",
pages = "71--83",
language = "zho",
abstract = "``跨语言代码生成旨在将英语到代码的生成能力迁移至其他自然语言。翻译-训 练(Translate-Train)和语码转换(Code-Switching)是实现跨语言迁移的两类经典数据增广方法,两者优势互补但尚未有效结合。为此,本文提出了一种面向跨语言代码生成的片段级语码转换(SpanCS)方法。首先,该方法利用语码转换框架关联源语言上下文与目标语言片段,以促进多种语言的交互和对齐。其次,该方法利用翻译-训练方法从完整的源语言翻译中提取目标语言片段,以保证增广数据与原始数据间的语义一致性。为了公平地评价多种自然语言之间代码生成的性能差异,本文通过人工翻译与校验,基于HumanEval构建了包含10种自然语言的多语言代码生成评测基MHumanEval。该基准上的三个主干模型的实验结果表明,SpanCS在跨语言代码生成任务上一致优于前人的数据增广方法。''"
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="qingfu-etal-2024-spancs">
<titleInfo>
<title>SpanCS:面向跨语言代码生成的片段级语码转换(SpanCS: Span-Level Code-Switching for Cross-Lingual Code Generation)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Qingfu</namePart>
<namePart type="family">Zhu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shiqi</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shuo</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhiming</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haoyu</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qiguang</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wanxiang</namePart>
<namePart type="family">Che</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">zho</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sun</namePart>
<namePart type="family">Maosong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liang</namePart>
<namePart type="family">Jiye</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Han</namePart>
<namePart type="family">Xianpei</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Liu</namePart>
<namePart type="family">Zhiyuan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">He</namePart>
<namePart type="family">Yulan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Taiyuan, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“跨语言代码生成旨在将英语到代码的生成能力迁移至其他自然语言。翻译-训 练(Translate-Train)和语码转换(Code-Switching)是实现跨语言迁移的两类经典数据增广方法,两者优势互补但尚未有效结合。为此,本文提出了一种面向跨语言代码生成的片段级语码转换(SpanCS)方法。首先,该方法利用语码转换框架关联源语言上下文与目标语言片段,以促进多种语言的交互和对齐。其次,该方法利用翻译-训练方法从完整的源语言翻译中提取目标语言片段,以保证增广数据与原始数据间的语义一致性。为了公平地评价多种自然语言之间代码生成的性能差异,本文通过人工翻译与校验,基于HumanEval构建了包含10种自然语言的多语言代码生成评测基MHumanEval。该基准上的三个主干模型的实验结果表明,SpanCS在跨语言代码生成任务上一致优于前人的数据增广方法。”</abstract>
<identifier type="citekey">qingfu-etal-2024-spancs</identifier>
<location>
<url>https://aclanthology.org/2024.ccl-1.6/</url>
</location>
<part>
<date>2024-07</date>
<extent unit="page">
<start>71</start>
<end>83</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T SpanCS:面向跨语言代码生成的片段级语码转换(SpanCS: Span-Level Code-Switching for Cross-Lingual Code Generation)
%A Zhu, Qingfu
%A Zhou, Shiqi
%A Wang, Shuo
%A Zhang, Zhiming
%A Wang, Haoyu
%A Chen, Qiguang
%A Che, Wanxiang
%Y Maosong, Sun
%Y Jiye, Liang
%Y Xianpei, Han
%Y Zhiyuan, Liu
%Y Yulan, He
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G zho
%F qingfu-etal-2024-spancs
%X “跨语言代码生成旨在将英语到代码的生成能力迁移至其他自然语言。翻译-训 练(Translate-Train)和语码转换(Code-Switching)是实现跨语言迁移的两类经典数据增广方法,两者优势互补但尚未有效结合。为此,本文提出了一种面向跨语言代码生成的片段级语码转换(SpanCS)方法。首先,该方法利用语码转换框架关联源语言上下文与目标语言片段,以促进多种语言的交互和对齐。其次,该方法利用翻译-训练方法从完整的源语言翻译中提取目标语言片段,以保证增广数据与原始数据间的语义一致性。为了公平地评价多种自然语言之间代码生成的性能差异,本文通过人工翻译与校验,基于HumanEval构建了包含10种自然语言的多语言代码生成评测基MHumanEval。该基准上的三个主干模型的实验结果表明,SpanCS在跨语言代码生成任务上一致优于前人的数据增广方法。”
%U https://aclanthology.org/2024.ccl-1.6/
%P 71-83
Markdown (Informal)
[SpanCS:面向跨语言代码生成的片段级语码转换(SpanCS: Span-Level Code-Switching for Cross-Lingual Code Generation)](https://aclanthology.org/2024.ccl-1.6/) (Zhu et al., CCL 2024)
ACL