Papers with query classification models
FABRIC: Fully-Automated Broad Intent Categorization in E-commerce (2025.emnlp-industry)
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| Challenge: | Existing query classification models have excellent predictive performance on single-intent queries, but there is little research on predicting multiple-intentions for broad queries. |
| Approach: | They propose to combine user click data, query-item relevance and LLM judgments to create an automatic method for multi-label e-commerce query classification. |
| Outcome: | The proposed method reduces the ambiguity of the annotations by blending the label assessment from three different sources: user click data, query-item relevance and LLM judgments. |
Reinforcement Learning for Adversarial Query Generation to Enhance Relevance in Cold-Start Product Search (2025.acl-industry)
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| Challenge: | Existing methods do not incorporate feedback from the query relevance model, limiting their ability to generate queries that enhance product retrieval. |
| Approach: | They propose an adversarial reinforcement learning framework that exposes weaknesses in query classification models by creating synthetic queries that augment the classifier's training set. |
| Outcome: | The proposed framework improves query generation performance on public datasets and on proprietary datasets. |