Papers by Eric Lehman
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? (2021.naacl-main)
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| Challenge: | Pretraining large (masked) language models over EHR data has yielded consistent performance gains across tasks. |
| Approach: | They propose to use large Transformers to release pretraining models over EHRs . they propose to recover patient names and conditions associated with them . |
| Outcome: | The proposed models recover patient names and conditions associated with patients . the proposed models share the model parameters for use by other researchers . |
Inferring Which Medical Treatments Work from Reports of Clinical Trials (N19-1)
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| Challenge: | Ideally, one would consult all available evidence from relevant clinical trials. however, these results are primarily disseminated in natural language scientific articles. |
| Approach: | They propose a task that involves inferring results from a full-text article describing randomized controlled trials with respect to a given intervention, comparator, and outcome of interest. |
| Outcome: | The proposed task consists of 10,000+ prompts coupled with full-text articles describing randomized controlled trials. |
ERASER: A Benchmark to Evaluate Rationalized NLP Models (2020.acl-main)
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Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, Byron C. Wallace
| Challenge: | State-of-the-art models in NLP are opaque in terms of how they come to make predictions. |
| Approach: | They propose to release a benchmark to measure the quality of rationales extracted by models and how faithful these rationale are to human annotators. |
| Outcome: | The proposed benchmark will enable researchers to compare models and track progress on interpretable models for NLP. |