@inproceedings{kumar-etal-2026-enhancing,
    title = "Enhancing User Safety: Context-Aware Detection of Offensive Query-Ad Pairs in Multimodal Search Advertising",
    author = "Kumar, Gaurav  and
      Xi, Qiangjian  and
      Dabral, Tanmaya Shekhar  and
      Ghasemi, Hooshang  and
      Krishnamoorthy, Abishek  and
      Fu, Danqing  and
      Min, Rui  and
      Antunez, Emilio  and
      Ding, Zhongli  and
      Narayana, Pradyumna",
    editor = {Matusevych, Yevgen  and
      Eryi{\u{g}}it, G{\"u}l{\c{s}}en  and
      Aletras, Nikolaos},
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-industry.36/",
    pages = "475--482",
    ISBN = "979-8-89176-384-5",
    abstract = "The proliferation of multi-modal online advertisements necessitates robust content moderation to ensure user safety, as offensive ad content can cause user distress and erode platform trust. This paper addresses the detection of content that becomes offensive only when a user{'}s search query is paired with a specific ad, a context-dependent challenge that simple moderation often misses. Key challenges include the nuanced, multi-modal nature of ads, severe data scarcity and class imbalance due to the rarity of offensive content, and the high cost of human labeling. To overcome these limitations, we introduce a novel, context-aware detection framework centered on a large-scale, Multi-modal Teacher-Student Knowledge Distillation architecture. A powerful Gemini encoder-only ``teacher'' model distills its knowledge into a lightweight student model suitable for low-latency deployment. We enhance robustness using a novel graph mining technique to find rare offensive examples for training. For evaluation, we developed a highly accurate Automated Evaluation Model (AEM){---}a separate, larger Gemini model utilizing Chain-of-Thought (CoT) reasoning{---}to rigorously assess performance in a live A/B test. Our results demonstrate that the proposed framework reduces the serving of offensive query-ad pairs by more than 80{\%} compared to the baseline, while maintaining the efficiency required for real-time advertising systems that operate at a scale of over $\approx100$ billion query-ad pairs per day. Disclaimer: This paper contains sentences and images that may be offensive. These examples are included solely for scientific analysis and do not reflect the views of the authors."
}