Papers with recall@1
SECRET: Semi-supervised Clinical Trial Document Similarity Search (2025.acl-long)
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| Challenge: | Clinical trials are resource-intensive, time-consuming and expensive to conduct, where errors in trial design, reduced efficacy, and safety events can result in significant delays, financial losses, and damage to reputation. |
| Approach: | They propose a method to summarize clinical trial protocols and search for similar trials based on a query protocol to identify similar historical trials. |
| Outcome: | The proposed method significantly outperforms baselines in recall@1 and precision@1 searches and zero-shot patient-trial matching, highlighting its superior utility in these tasks. |
PhotoChat: A Human-Human Dialogue Dataset With Photo Sharing Behavior For Joint Image-Text Modeling (2021.acl-long)
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| Challenge: | PhotoChat contains 12k dialogues, each of which is paired with a user photo that is shared during the conversation. |
| Approach: | They propose to use PhotoChat to facilitate research on image-text modeling by combining a photo-sharing intent prediction task and a picture retrieval task to retrieve the most relevant photo according to the dialogue context. |
| Outcome: | The proposed tasks achieve 10.4% recall@1 and 58.1% F1 scores, indicating that the proposed dataset presents interesting yet challenging real-world problems. |
Show, Write, and Retrieve: Entity-aware Article Generation and Retrieval (2023.findings-emnlp)
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| Challenge: | Prior work typically encodes all tokens in articles using pre-trained language models, however, many named entities are difficult to accurately recognize and predict by language models. |
| Approach: | They propose an ENtity-aware article GeneratIoN and rEtrieval framework to explicitly incorporate named entities into language models. |
| Outcome: | The proposed framework can boost article generation and retrieval performance, with a 4-5 perplexity improvement in article generation, and a 3-4% boost in recall@1 in article retrieval. |