@inproceedings{chen-etal-2026-towards-autopilot,
title = "Towards Autopilot Labeling for Short Video Content Governance",
author = "Chen, Junwen and
Wang, Hongwei and
Wang, Zixuan and
Wang, Jingbing and
Liu, Mingchao and
Siong, Ong Sheng and
Zang, Xiaojiao and
Chao, Ju-Chin and
Jin, Ze and
Men, Kaiwen and
Li, Yin and
Li, Jingwei and
Hao, Zhuolin and
Xiong, Hongyu and
Song, Yang",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.58/",
pages = "960--974",
ISBN = "979-8-89176-460-6",
abstract = "The explosive growth of short videos has presented significant challenges to content governance. Traditional manual labeling is inefficient, slow to iterate, and costly. Therefore, we explore and develop a multi-stage autopilot labeling framework aimed at progressively increasing the level of automation in short video content annotation. To move beyond the heavy reliance on human expert labeling at the initial L0 stage, we first introduce a discriminative MLLM labeler at the L1 stage, which is fine-tuned with supervised data to directly predict labels for short videos. Advancing to the L2 stage, we shift to a domain-adaptive MLLM pretrained on short video data and use it for zero-shot labeling, enabling flexible generalization without task-specific supervision. In the L3 stage, we propose an agentic annotation workflow, and equip the agent with several key tools, which empower the agent to handle complex, unfamiliar, or vaguely defined cases, significantly reducing the need for human intervention. We conduct extensive experiments across various content issues, and results demonstrate that each stage of the autopilot labeling framework progressively improves labeling accuracy, and has significantly reduced manual labor costs in real-world applications by up to 85{\%}. This automated labeling approach is poised to gradually replace manual annotation, emerging as the new paradigm for short video content labeling."
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<abstract>The explosive growth of short videos has presented significant challenges to content governance. Traditional manual labeling is inefficient, slow to iterate, and costly. Therefore, we explore and develop a multi-stage autopilot labeling framework aimed at progressively increasing the level of automation in short video content annotation. To move beyond the heavy reliance on human expert labeling at the initial L0 stage, we first introduce a discriminative MLLM labeler at the L1 stage, which is fine-tuned with supervised data to directly predict labels for short videos. Advancing to the L2 stage, we shift to a domain-adaptive MLLM pretrained on short video data and use it for zero-shot labeling, enabling flexible generalization without task-specific supervision. In the L3 stage, we propose an agentic annotation workflow, and equip the agent with several key tools, which empower the agent to handle complex, unfamiliar, or vaguely defined cases, significantly reducing the need for human intervention. We conduct extensive experiments across various content issues, and results demonstrate that each stage of the autopilot labeling framework progressively improves labeling accuracy, and has significantly reduced manual labor costs in real-world applications by up to 85%. This automated labeling approach is poised to gradually replace manual annotation, emerging as the new paradigm for short video content labeling.</abstract>
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%0 Conference Proceedings
%T Towards Autopilot Labeling for Short Video Content Governance
%A Chen, Junwen
%A Wang, Hongwei
%A Wang, Zixuan
%A Wang, Jingbing
%A Liu, Mingchao
%A Siong, Ong Sheng
%A Zang, Xiaojiao
%A Chao, Ju-Chin
%A Jin, Ze
%A Men, Kaiwen
%A Li, Yin
%A Li, Jingwei
%A Hao, Zhuolin
%A Xiong, Hongyu
%A Song, Yang
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F chen-etal-2026-towards-autopilot
%X The explosive growth of short videos has presented significant challenges to content governance. Traditional manual labeling is inefficient, slow to iterate, and costly. Therefore, we explore and develop a multi-stage autopilot labeling framework aimed at progressively increasing the level of automation in short video content annotation. To move beyond the heavy reliance on human expert labeling at the initial L0 stage, we first introduce a discriminative MLLM labeler at the L1 stage, which is fine-tuned with supervised data to directly predict labels for short videos. Advancing to the L2 stage, we shift to a domain-adaptive MLLM pretrained on short video data and use it for zero-shot labeling, enabling flexible generalization without task-specific supervision. In the L3 stage, we propose an agentic annotation workflow, and equip the agent with several key tools, which empower the agent to handle complex, unfamiliar, or vaguely defined cases, significantly reducing the need for human intervention. We conduct extensive experiments across various content issues, and results demonstrate that each stage of the autopilot labeling framework progressively improves labeling accuracy, and has significantly reduced manual labor costs in real-world applications by up to 85%. This automated labeling approach is poised to gradually replace manual annotation, emerging as the new paradigm for short video content labeling.
%U https://aclanthology.org/2026.emnlp-industry.58/
%P 960-974
Markdown (Informal)
[Towards Autopilot Labeling for Short Video Content Governance](https://aclanthology.org/2026.emnlp-industry.58/) (Chen et al., EMNLP 2026)
ACL
- Junwen Chen, Hongwei Wang, Zixuan Wang, Jingbing Wang, Mingchao Liu, Ong Sheng Siong, Xiaojiao Zang, Ju-Chin Chao, Ze Jin, Kaiwen Men, Yin Li, Jingwei Li, Zhuolin Hao, Hongyu Xiong, and Yang Song. 2026. Towards Autopilot Labeling for Short Video Content Governance. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 960–974, Budapest, Hungary. Association for Computational Linguistics.