Search Results for author:
Found 17 papers, 10 papers with code
How Many and Which Training Points Would Need to be Removed to Flip this Prediction?
We consider the problem of identifying a minimal subset of training data $\mathcal{S}_t$ such that if the instances comprising $\mathcal{S}_t$ had been removed prior to training, the categorization of a given test point $x_t$ would have been different.
Influence Functions for Sequence Tagging Models
We show the practical utility of segment influence by using the method to identify systematic annotation errors in two named entity recognition corpora.
Modular Self-Supervision for Document-Level Relation Extraction
Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining high recall of the latest findings is crucial for practical applications.
Combining Feature and Instance Attribution to Detect Artifacts
In this paper we evaluate use of different attribution methods for aiding identification of training data artifacts.
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
The cost of training such models (and the necessity of data access to do so) coupled with their utility motivates parameter sharing, i. e., the release of pretrained models such as ClinicalBERT.
An Empirical Comparison of Instance Attribution Methods for NLP
Instance attribution methods constitute one means of accomplishing these goals by retrieving training instances that (may have) led to a particular prediction.
SciREX: A Challenge Dataset for Document-Level Information Extraction
It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections.
Learning to Faithfully Rationalize by Construction
In NLP this often entails extracting snippets of an input text `responsible for' corresponding model output; when such a snippet comprises tokens that indeed informed the model's prediction, it is a faithful explanation.
ERASER: A Benchmark to Evaluate Rationalized NLP Models
We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are (i. e., the degree to which provided rationales influenced the corresponding predictions).
SUPP.AI: Finding Evidence for Supplement-Drug Interactions
We fine-tune the contextualized word representations of the RoBERTa language model using labeled DDI data, and apply the fine-tuned model to identify supplement interactions.
Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data
We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within the coming three-month period.
An Analysis of Attention over Clinical Notes for Predictive Tasks
In this work we perform experiments to explore this question using two EMR corpora and four different predictive tasks, that: (i) inclusion of attention mechanisms is critical for neural encoder modules that operate over notes fields in order to yield competitive performance, but, (ii) unfortunately, while these boost predictive performance, it is decidedly less clear whether they provide meaningful support for predictions.
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
We propose a method for learning disentangled representations of texts that code for distinct and complementary aspects, with the aim of affording efficient model transfer and interpretability.
Structured Disentangled Representations
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner.
