Papers by Pranav Kasela
Denoising Attention for Query-aware User Modeling (2024.findings-naacl)
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| Challenge: | Recent work has proposed to build user models at query time by leveraging the Attention mechanism, which allows weighing the contribution of the user-related information w.r.t. the current query. |
| Approach: | They propose to use the Attention mechanism to build user models at query time by weighing the contribution of the user-related information w.r.t. the Attention variant adopts a robust normalization scheme and introduces . filtering mechanism to better discern among the user related data those helpful for personalization. |
| Outcome: | The proposed approach improves MAP, MRR, and NDCG above 15% w.r.t. other Attention variants at the state-of-the-art. |
Leveraging Cognitive Complexity of Texts for Contextualization in Dense Retrieval (2025.emnlp-main)
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| Challenge: | Existing approaches to estimate semantic similarity of queries and documents rely on token-level information derived from query/document interactions. |
| Approach: | They propose a new DRM that leverages query/document interactions based on full embedding representations generated by a Transformer-based model. |
| Outcome: | The proposed model outperforms fine-tuning techniques on lightweight bi-encoders and traditional late-interaction models. |