Search Results for author: Byron C. Wallace

Found 54 papers, 28 papers with code

Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges

no code implementations7 Mar 2023 Sanjana Ramprasad, Denis Jered McInerney, Iain J. Marshal, Byron C. Wallace

We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query.

Document Summarization Multi-Document Summarization

CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models

no code implementations23 Feb 2023 Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace

This approach has the promise to empower physicians to use their domain expertise to craft features which are clinically meaningful for a downstream task of interest, without having to manually extract these from raw EHR (as often done now).

NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization

1 code implementation11 Feb 2023 Junru Lu, Jiazheng Li, Byron C. Wallace, Yulan He, Gabriele Pergola

In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved.

Semantic Similarity Semantic Textual Similarity +1

How Many and Which Training Points Would Need to be Removed to Flip this Prediction?

1 code implementation4 Feb 2023 Jinghan Yang, Sarthak Jain, Byron C. Wallace

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.

text-classification Text Classification

Do Multi-Document Summarization Models Synthesize?

no code implementations31 Jan 2023 Jay DeYoung, Stephanie C. Martinez, Iain J. Marshall, Byron C. Wallace

In this paper we ask: To what extent do modern multi-document summarization models implicitly perform this type of synthesis?

Document Summarization Multi-Document Summarization

Intermediate Entity-based Sparse Interpretable Representation Learning

1 code implementation3 Dec 2022 Diego Garcia-Olano, Yasumasa Onoe, Joydeep Ghosh, Byron C. Wallace

However, while fine-tuning sparse, interpretable representations improves accuracy on downstream tasks, it destroys the semantics of the dimensions which were enforced in pre-training.

Representation Learning

Influence Functions for Sequence Tagging Models

1 code implementation25 Oct 2022 Sarthak Jain, Varun Manjunatha, Byron C. Wallace, Ani Nenkova

We show the practical utility of segment influence by using the method to identify systematic annotation errors in two named entity recognition corpora.

named-entity-recognition Named Entity Recognition +3

PHEE: A Dataset for Pharmacovigilance Event Extraction from Text

1 code implementation22 Oct 2022 Zhaoyue Sun, Jiazheng Li, Gabriele Pergola, Byron C. Wallace, Bino John, Nigel Greene, Joseph Kim, Yulan He

The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions.

Event Extraction

Self-Repetition in Abstractive Neural Summarizers

no code implementations14 Oct 2022 Nikita Salkar, Thomas Trikalinos, Byron C. Wallace, Ani Nenkova

In a regression analysis, we find that the three architectures have different propensities for repeating content across output summaries for inputs, with BART being particularly prone to self-repetition.

That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data

no code implementations12 Oct 2022 Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace

We compare alignments from a state-of-the-art multimodal (image and text) model for EHR with human annotations that link image regions to sentences.

Evaluating Factuality in Text Simplification

1 code implementation ACL 2022 Ashwin Devaraj, William Sheffield, Byron C. Wallace, Junyi Jessy Li

We find that errors often appear in both that are not captured by existing evaluation metrics, motivating a need for research into ensuring the factual accuracy of automated simplification models.

Text Simplification

What Would it Take to get Biomedical QA Systems into Practice?

no code implementations EMNLP (MRQA) 2021 Gregory Kell, Iain J. Marshall, Byron C. Wallace, Andre Jaun

Medical question answering (QA) systems have the potential to answer clinicians uncertainties about treatment and diagnosis on demand, informed by the latest evidence.

Question Answering

Combining Feature and Instance Attribution to Detect Artifacts

no code implementations Findings (ACL) 2022 Pouya Pezeshkpour, Sarthak Jain, Sameer Singh, Byron C. Wallace

In this paper we evaluate use of different attribution methods for aiding identification of training data artifacts.

Biomedical Interpretable Entity Representations

2 code implementations Findings (ACL) 2021 Diego Garcia-Olano, Yasumasa Onoe, Ioana Baldini, Joydeep Ghosh, Byron C. Wallace, Kush R. Varshney

Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretable.

Entity Disambiguation Representation Learning

Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?

2 code implementations NAACL 2021 Eric Lehman, Sarthak Jain, Karl Pichotta, Yoav Goldberg, Byron C. Wallace

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.

Disentangling Representations of Text by Masking Transformers

no code implementations EMNLP 2021 Xiongyi Zhang, Jan-Willem van de Meent, Byron C. Wallace

Representations from large pretrained models such as BERT encode a range of features into monolithic vectors, affording strong predictive accuracy across a multitude of downstream tasks.


On the Impact of Random Seeds on the Fairness of Clinical Classifiers

no code implementations NAACL 2021 Silvio Amir, Jan-Willem van de Meent, Byron C. Wallace

Recent work has shown that fine-tuning large networks is surprisingly sensitive to changes in random seed(s).


Paragraph-level Simplification of Medical Texts

1 code implementation NAACL 2021 Ashwin Devaraj, Iain J. Marshall, Byron C. Wallace, Junyi Jessy Li

In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics.

Language Modelling

An Empirical Comparison of Instance Attribution Methods for NLP

1 code implementation NAACL 2021 Pouya Pezeshkpour, Sarthak Jain, Byron C. Wallace, Sameer Singh

Instance attribution methods constitute one means of accomplishing these goals by retrieving training instances that (may have) led to a particular prediction.


Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data

no code implementations EMNLP 2021 David Lowell, Brian E. Howard, Zachary C. Lipton, Byron C. Wallace

Unsupervised Data Augmentation (UDA) is a semi-supervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding 'noised' examples produced via data augmentation.

Data Augmentation text-classification +2

Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations

no code implementations7 Oct 2020 Benjamin E. Nye, Jay DeYoung, Eric Lehman, Ani Nenkova, Iain J. Marshall, Byron C. Wallace

Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction).

Decision Making Relation Extraction

Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization

2 code implementations25 Aug 2020 Byron C. Wallace, Sayantan Saha, Frank Soboczenski, Iain J. Marshall

We enlist medical professionals to evaluate generated summaries, and we find that modern summarization systems yield consistently fluent and relevant synopses, but that they are not always factual.

Abstractive Text Summarization Document Summarization +1

Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time

1 code implementation ACL 2020 Benjamin E. Nye, Ani Nenkova, Iain J. Marshall, Byron C. Wallace

We apply the system at scale to all reports of randomized controlled trials indexed in MEDLINE, powering the automatic generation of evidence maps, which provide a global view of the efficacy of different interventions combining data from all relevant clinical trials on a topic.

Semi-Automating Knowledge Base Construction for Cancer Genetics

no code implementations AKBC 2020 Somin Wadhwa, Kanhua Yin, Kevin S. Hughes, Byron C. Wallace

We propose and evaluate several model variants, including a transformer-based joint entity and relation extraction model to extract <germline mutation, risk-estimate>} pairs.

Joint Entity and Relation Extraction

Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions

1 code implementation ACL 2020 Xiaochuang Han, Byron C. Wallace, Yulia Tsvetkov

In this work, we investigate the use of influence functions for NLP, providing an alternative approach to interpreting neural text classifiers.

Natural Language Inference

Evidence Inference 2.0: More Data, Better Models

1 code implementation WS 2020 Jay DeYoung, Eric Lehman, Ben Nye, Iain J. Marshall, Byron C. Wallace

Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions.

Learning to Faithfully Rationalize by Construction

2 code implementations ACL 2020 Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, Byron C. Wallace

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.

Feature Importance text-classification +1

Query-Focused EHR Summarization to Aid Imaging Diagnosis

no code implementations9 Apr 2020 Denis Jered McInerney, Borna Dabiri, Anne-Sophie Touret, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace

We propose and evaluate models that extract relevant text snippets from patient records to provide a rough case summary intended to aid physicians considering one or more diagnoses.

Extractive Summarization

Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve

no code implementations CL (ACL) 2021 Oshin Agarwal, Yinfei Yang, Byron C. Wallace, Ani Nenkova

We examine these questions by contrasting the performance of several variants of LSTM-CRF architectures for named entity recognition, with some provided only representations of the context as features.

named-entity-recognition Named Entity Recognition +1

Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models

1 code implementation8 Apr 2020 Oshin Agarwal, Yinfei Yang, Byron C. Wallace, Ani Nenkova

We propose a method for auditing the in-domain robustness of systems, focusing specifically on differences in performance due to the national origin of entities.

Fairness named-entity-recognition +2

ERASER: A Benchmark to Evaluate Rationalized NLP Models

2 code implementations ACL 2020 Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, Byron C. Wallace

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).

Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data

no code implementations28 Jun 2019 Ramin Mohammadi, Sarthak Jain, Stephen Agboola, Ramya Palacholla, Sagar Kamarthi, Byron C. Wallace

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.

Management regression

Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction

no code implementations NAACL 2019 Yinfei Yang, Oshin Agarwal, Chris Tar, Byron C. Wallace, Ani Nenkova

Experiments on a complex biomedical information extraction task using expert and lay annotators show that: (i) simply excluding from the training data instances predicted to be difficult yields a small boost in performance; (ii) using difficulty scores to weight instances during training provides further, consistent gains; (iii) assigning instances predicted to be difficult to domain experts is an effective strategy for task routing.

An Analysis of Attention over Clinical Notes for Predictive Tasks

no code implementations WS 2019 Sarthak Jain, Ramin Mohammadi, Byron C. Wallace

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.

Inferring Which Medical Treatments Work from Reports of Clinical Trials

2 code implementations NAACL 2019 Eric Lehman, Jay DeYoung, Regina Barzilay, Byron C. Wallace

In this paper, we present a new task and corpus for making this unstructured evidence actionable.

Structured Neural Topic Models for Reviews

no code implementations12 Dec 2018 Babak Esmaeili, Hongyi Huang, Byron C. Wallace, Jan-Willem van de Meent

We present Variational Aspect-based Latent Topic Allocation (VALTA), a family of autoencoding topic models that learn aspect-based representations of reviews.

Topic Models

Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding

1 code implementation EMNLP 2018 Gaurav Singh, James Thomas, Iain J. Marshall, John Shawe-Taylor, Byron C. Wallace

We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i. e., an ontology).

Practical Obstacles to Deploying Active Learning

no code implementations IJCNLP 2019 David Lowell, Zachary C. Lipton, Byron C. Wallace

Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget.

Active Learning Text Classification

Learning Disentangled Representations of Texts with Application to Biomedical Abstracts

1 code implementation EMNLP 2018 Sarthak Jain, Edward Banner, Jan-Willem van de Meent, Iain J. Marshall, Byron C. Wallace

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.


Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness

no code implementations21 Sep 2017 Zhiguo Yu, Byron C. Wallace, Todd Johnson, Trevor Cohen

In this paper, we present a method that retrofits distributional context vector representations of biomedical concepts using structural information from the UMLS Metathesaurus, such that the similarity between vector representations of linked concepts is augmented.

Semantic Similarity Semantic Textual Similarity

Quantifying Mental Health from Social Media with Neural User Embeddings

1 code implementation30 Apr 2017 Silvio Amir, Glen Coppersmith, Paula Carvalho, Mário J. Silva, Byron C. Wallace

Our experimental results demonstrate that the user embeddings capture similarities between users with respect to mental conditions, and are predictive of mental health.

Representation Learning

Neural Information Retrieval: A Literature Review

no code implementations18 Nov 2016 Ye Zhang, Md Mustafizur Rahman, Alex Braylan, Brandon Dang, Heng-Lu Chang, Henna Kim, Quinten McNamara, Aaron Angert, Edward Banner, Vivek Khetan, Tyler McDonnell, An Thanh Nguyen, Dan Xu, Byron C. Wallace, Matthew Lease

A recent "third wave" of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing.

Information Retrieval Retrieval +2

Active Discriminative Text Representation Learning

1 code implementation14 Jun 2016 Ye Zhang, Matthew Lease, Byron C. Wallace

We also show that, as expected, the method quickly learns discriminative word embeddings.

Active Learning Document Classification +4

Rationale-Augmented Convolutional Neural Networks for Text Classification

2 code implementations EMNLP 2016 Ye Zhang, Iain Marshall, Byron C. Wallace

We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their component sentences.

General Classification text-classification +1

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