Papers by Xiangji Zeng
AdDriftBench: A Benchmark for Detecting Data Drift and Label Drift in Short Video Advertising (2025.findings-emnlp)
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Yinghao Song, Xiangji Zeng, Shuai Cui, Lu Sun, Zhaowei Liu, Yuan Yuan, Yulu Wang, Hai Zhou, Zhaohan Gong
| Challenge: | Short video advertising scenarios present unique challenges due to data drift (DD) and label drift (LD). |
| Approach: | They propose to use data drift and label drift to evaluate models under rapidly shifting content distributions and labeling scenarios to assess their generalization capabilities. |
| Outcome: | The proposed model performs moderately in short video advertising contexts, particularly in handling fine-grained semantics and adapting to shifting instructions. |
KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference (2021.acl-long)
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| Challenge: | Existing approaches in NLP focus on “WHY A” rather than contrastive “WHA NOT B” Experimental results show that contrastive explanations are beneficial to fit the scenarios by clarifying the difference between the predicted answer and other possible wrong ones. |
| Approach: | They propose to generate contrastive explanations with counterfactual examples in NLI by identifying key phrases from input sentences and using them as key perturbations to generate counterfacts. |
| Outcome: | The proposed framework improves on SNLI and ETPA models by 91.9%. |
Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition (2020.emnlp-main)
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| Challenge: | Using labeled data, named entity recognition is labor-intensive, time-consuming and expensive. |
| Approach: | They propose a method which decomposes named entity into two parts: entity and context. |
| Outcome: | The proposed method improves the generalization ability of models under limited observational examples. |