Papers by Tristan Naumann
DocLens: Multi-aspect Fine-grained Medical Text Evaluation (2024.acl-long)
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Yiqing Xie, Sheng Zhang, Hao Cheng, Pengfei Liu, Zelalem Gero, Cliff Wong, Tristan Naumann, Hoifung Poon, Carolyn Rose
| Challenge: | Medical text generation systems are widely used to assist with administrative work and highlight salient information to support decision-making. |
| Approach: | They propose a set of metrics to evaluate completeness, conciseness, and attribution of medical text at a fine-grained level. |
| Outcome: | The proposed framework exhibits substantially higher agreement with medical experts than existing metrics. |
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction (2023.acl-long)
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| Challenge: | Relation extraction (RE) has been challenging in low-resource domains and with limited resources. |
| Approach: | They propose to pretrain and finetune the RE model using consistent objectives of contrastive learning. |
| Outcome: | The proposed method outperforms PLM-based RE classifier on two document-level RE datasets. |
Knowledge-Rich Self-Supervision for Biomedical Entity Linking (2022.findings-emnlp)
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Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
| Challenge: | Entity linking is challenging in high-value domains with myriad entities . standard classification approaches suffer from the annotation bottleneck . |
| Approach: | They propose a self-supervised approach to learn domain knowledge for biomedical entity linking . it generates self-reported mention examples on unlabeled text and trains contextual encoder . |
| Outcome: | The proposed method outperforms existing methods by 20 points in accuracy on biomedical datasets. |
Compositional Zero-Shot Domain Transfer with Text-to-Text Models (2023.tacl-1)
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Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland
| Challenge: | Existing approaches to zero-shot domain transfer are limited by domain gap and lack of in-domain labels. |
| Approach: | They propose a compositional transfer learning framework (DoT51) that learns domain knowledge and task knowledge in a multi-task manner without access to in-domain labels. |
| Outcome: | The proposed framework outperforms the current state-of-the-art in zero-shot domain transfer by over 7 absolute points in accuracy on RadNLI. |
What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization (2023.acl-long)
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Griffin Adams, Bichlien Nguyen, Jake Smith, Yingce Xia, Shufang Xie, Anna Ostropolets, Budhaditya Deb, Yuan-Jyue Chen, Tristan Naumann, Noémie Elhadad
| Challenge: | Summarization models are trained to maximize the likelihood of a single reference (MLE) but little is known about why one setup is more effective than another . |
| Approach: | They add a calibration step which exposes a model to its own ranked outputs to improve relevance or contrasts positive and negative sets to improve faithfulness. |
| Outcome: | The proposed calibration step can unlock large gains in relevance or faithfulness. |
Modular Self-Supervision for Document-Level Relation Extraction (2021.emnlp-main)
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| Challenge: | Prior work on information extraction tends to focus on binary relations within sentences . practical applications often require extracting complex relations across large text spans . |
| Approach: | They propose to decompose document-level relation extraction into relation detection and argument resolution, taking inspiration from Davidsonian semantics. |
| Outcome: | The proposed method outperforms state-of-the-art methods in biomedical machine reading for precision oncology by 20 absolute F1 points. |