Papers by Ramraj Chandradevan
DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation (2024.naacl-long)
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| Challenge: | State-of-the-art rankers pre-trained on large task-specific training data such as MS-MARCO exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot. |
| Approach: | They propose a method to generate unsupervised domain adaptation for ranking using large-scale task-specific training data such as MS-MARCO and Wikipedia retrieval. |
| Outcome: | The proposed method outperforms all zero-shot baselines and significantly outperfies the SOTA baselines on 16 out of 18 datasets, for an average of 4% relative improvement across all datasets. |
QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration (2024.naacl-demo)
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| Challenge: | Formulating effective search queries can be a daunting task for users when they lack expertise in a specific domain or are not proficient in the language of the content. |
| Approach: | QueryExplorer is an interactive query generation, reformulation, and retrieval interface with support for Hug-gingFace generation models and PyTerrier’sretrieval pipelines and datasets. |
| Outcome: | QueryExplorer is an interactive query generation, reformulation, and retrieval interface with support for Hug-gingFace generation models and PyTerrier’sretrieval pipelines and datasets, and extensivelogging of human feedback. |