Papers by Gabriella Pasi
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. |
IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages (2021.emnlp-main)
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Rexhina Blloshmi, Tommaso Pasini, Niccolò Campolungo, Somnath Banerjee, Roberto Navigli, Gabriella Pasi
| Challenge: | Recent advances in contextualized embeddings have made ranking on non-English documents cumbersome . a novel multilingual query expansion mechanism provides sense definitions as additional semantic information for the query. |
| Approach: | They propose a multilingual query expansion mechanism that leverages word sense information to enhance the model's performance. |
| Outcome: | The proposed model performs better than its supervised and unsupervised alternatives across languages while being trained on English Robust04 data. |
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. |
AdaKron: An Adapter-based Parameter Efficient Model Tuning with Kronecker Product (2024.lrec-main)
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| Challenge: | Large Pretrained Language Models (PLMs) have billions of parameters, causing computational challenges to fine-tuning models. |
| Approach: | They propose an Adapter-based fine-tuning with the Kronecker product that combine the outputs of two small networks to form a final vector whose dimension is the product of the dimensions of the individual outputs. |
| Outcome: | The proposed method achieves the same performance levels as state-of-the-art methods on the GLUE benchmark . |