Papers by Zhongping Zhang
Real, Fake, or Manipulated? Detecting Machine-Influenced Text (2025.findings-emnlp)
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| Challenge: | Prior work on machine generated text detection focused on identifying whether document was human or machine written, ignoring these fine-grained uses. |
| Approach: | They propose a machine-influenced text detector that learns to separate text samples from four primary types . the detector uses a subcategory guidance module to help separate the fine-grained categories . |
| Outcome: | The proposed detector outperforms the state-of-the-art in five LLMs and six domains. |
MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)
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Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li
| Challenge: | In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming. |
| Approach: | They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space. |
| Outcome: | The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis. |
Machine-Generated Text Localization (2024.findings-acl)
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| Challenge: | Prior work focused on identifying only part of a document as machine or human written . a key challenge is that short spans of text provide little information indicating if it is machine generated due to its short length . |
| Approach: | They propose a method that localizes the portions of a document that were machine generated. |
| Outcome: | The proposed method can detect changes in style or content to boost performance. |
Show, Write, and Retrieve: Entity-aware Article Generation and Retrieval (2023.findings-emnlp)
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| Challenge: | Prior work typically encodes all tokens in articles using pre-trained language models, however, many named entities are difficult to accurately recognize and predict by language models. |
| Approach: | They propose an ENtity-aware article GeneratIoN and rEtrieval framework to explicitly incorporate named entities into language models. |
| Outcome: | The proposed framework can boost article generation and retrieval performance, with a 4-5 perplexity improvement in article generation, and a 3-4% boost in recall@1 in article retrieval. |