Shijie Liu

Shijie Liu

he/him/his

PhD researcher in computational linguistics, computational terminology (ATE), and translation technology

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Pages

Posts

Future Blog Post

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Blog Post number 1

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portfolio

publications

ChatGPT人机对话式翻译研究

Published in 上海理工大学学报（社会科学版）, 2024

聚焦ChatGPT人机对话式翻译，分析其译文特征并通过与以谷歌翻译为代表的神经机器翻译质量对比发现二者差距。

Recommended citation: 李梅, 孔德璐. (2024). "ChatGPT人机对话式翻译研究." 上海理工大学学报（社会科学版）.
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新质生产力背景下中国网络文学的国际传播与外译挑战——以《斗破苍穹》为例

Published in 翻译与传播, 2024

本文以《斗破苍穹》为个案，探讨中国网络文学在“新质生产力”背景下的国际传播与外译挑战，分析其全球化路径、文化适配过程及未来融合新兴技术的发展可能性。

Recommended citation: 孔德璐, 李宝虎, 何婷. (2024). "新质生产力背景下中国网络文学的国际传播与外译挑战——以《斗破苍穹》为例." 翻译与传播, (2): 63–74.
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基于机器学习的政经语篇人机翻译风格研究——以《国富论》中译本为例

Published in 外语导刊, 2025

本文利用机器学习算法，基于自建《国富论》人机多译本平行语料库，通过分类、聚类、特征选择实验，考察人类专家译本与大语言模型机器译本的风格差异。

Recommended citation: 孔德璐. (2025). "基于机器学习的政经语篇人机翻译风格研究——以《国富论》中译本为例." 外语导刊. 48(1).
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Decoding Machine Translationese in English-Chinese News: LLMs vs. NMTs

Published in MT Summit 2025 (Technical Track), arXiv preprint, 2025

This study explores Machine Translationese (MTese) in English-to-Chinese news translation using classification and clustering with a five-layer feature set.

Recommended citation: Kong, D., & Macken, L. (2025). "Decoding Machine Translationese in English-Chinese News: LLMs vs. NMTs." MT Summit 2025 (Technical Track), arXiv preprint arXiv:2506.22050.
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Can Peter Pan Survive MT? A Stylometric Study of LLMs, NMTs, and HTs in Children’s Literature Translation

Published in CTT Workshop @ MT Summit 2025, arXiv preprint, 2025

A stylometric study of machine vs. human translations of Peter Pan using 447 linguistic features across generic and creative-text dimensions.

Recommended citation: Kong, D., & Macken, L. (2025). "Can Peter Pan Survive MT? A Stylometric Study of LLMs, NMTs, and HTs in Children Literature Translation." CTT Workshop @ MT Summit 2025, arXiv preprint arXiv:2506.22038.
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talks

Detecting Language Features of ‘Machine-Translationese’ through Machine Learning: Original Chinese News Text vs. Machine-Translated Chinese News Text

Published:

Abstract:
This presentation investigates the linguistic features of “Machine-Translationese” (MTese) in English-to-Chinese machine-translated news texts. Through machine learning approaches, we identify and quantify distinctive characteristics that differentiate machine-translated Chinese news texts from originally authored Chinese news texts. Our methodology employs clustering techniques to visualize these differentiating features, revealing systematic patterns in MTese.

Can Peter Pan Survive MT? A Stylometric Study of LLMs, NMTs, and HTs in Children’s Literature Translation

Published:

This study evaluates the performance of machine translations (MTs) versus human translations (HTs) in English-to-Chinese children’s literature translation (CLT) from a stylometric perspective. A Peter Pan corpus was constructed, including 21 translations: 7 HTs, 7 large language model translations (LLMs), and 7 neural machine translation outputs (NMTs). The analysis uses both a generic feature set (lexical, syntactic, readability, n-gram features) and a creative text translation (CTT-specific) feature set (repetition, rhythm, translatability, miscellaneous), totaling 447 linguistic features.

Stylometric analysis using classification and clustering techniques reveals that, for generic features, HTs and MTs differ significantly in conjunction word distributions and the ratio of 1-word-gram-YiYang. NMTs and LLMs differ in descriptive word usage and adverb ratios. For CTT-specific features, LLMs outperform NMTs in distribution and align more closely with HTs in stylistic characteristics, highlighting the potential of LLMs in CLT.

Comments: 19 pages, 8 figures, 4 tables. Accepted at the 2nd Workshop on Creative-text Translation and Technology, co-located with MT Summit 2025. Official paper may later be accessed from ACL Anthology.

PPT: Link

Decoding Machine Translationese in English-Chinese News: LLMs vs. NMTs

Published:

This study explores Machine Translationese (MTese)—the linguistic peculiarities of machine translation outputs—focusing on the under-researched English-to-Chinese language pair in news texts. We construct a large dataset consisting of 4 sub-corpora and employ a comprehensive five-layer feature set. A chi-square ranking algorithm is applied for feature selection in both classification and clustering tasks. Our findings confirm the presence of MTese in both Neural Machine Translation systems (NMTs) and Large Language Models (LLMs). Original Chinese texts are nearly perfectly distinguishable from both LLM and NMT outputs. Notable linguistic patterns in MT outputs are shorter sentence lengths and increased use of adversative conjunctions. Comparing LLMs and NMTs, we achieve approximately 70% classification accuracy, with LLMs exhibiting greater lexical diversity and NMTs using more brackets. Additionally, translation-specific LLMs show lower lexical diversity but higher usage of causal conjunctions compared to generic LLMs. Lastly, we find no significant differences between LLMs developed by Chinese firms and their foreign counterparts.

Comments: 14 pages, 5 figures, 6 tables. Accepted in MT Summit 2025, Research: Technical track. Official version may be accessed later in the ACL Anthology.

PPT: Download here

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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