Entity-Enriched Neural Models for Clinical Question Answering

Rawat, Bhanu Pratap Singh; Weng, Wei-Hung; Min, So Yeon; Raghavan, Preethi; Szolovits, Peter

doi:10.48550/arxiv.2005.06587

Cited by 2 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…as an important task to assist clinical practitioners [Patrick and Li, 2012, Pampari et al, 2018, Fan, 2019, Rawat et al, 2020. Neural QA models in recent years , Devlin et al, 2019b show promising results in this research.…”

Section: Introductionmentioning

confidence: 64%

Towards More Robust Natural Language Understanding

Zhang¹

2021

Preprint

View full text Add to dashboard Cite

Natural Language Understanding (NLU) is a branch of Natural Language Processing (NLP) that uses intelligent computer software to understand texts that encode human knowledge. Recent years have witnessed notable progress across various NLU tasks with deep learning techniques, especially with pretrained language models. Besides proposing more advanced model architectures, constructing more reliable and trustworthy datasets also plays a huge role in improving NLU systems, without which it would be impossible to train a decent NLU model. It's worth noting that the human ability of understanding natural language is flexible and robust. On the contrary, most of existing NLU systems fail to achieve desirable performance on out-of-domain data or struggle on handling challenging I feel incredibly fortunate to have Dr. Huan Sun as my advisor, without whom nothing in this thesis is possible. I would like to express my sincere gratitude to her for critiquing my work and my ideas in a constructive way. Her vision and rigorous research attitudes have shaped my thoughts. I am always indebted to her for guiding me all the way, for her contagious energy, for being so supportive and caring about students.I owe a great debt of gratitude to Dr. Marie-Catherine de Marneffe for her countless help. I am super grateful for her many invaluable insights and suggestions on my work. She has been so generous with her time, reading, reviewing and commenting on many of my writings. I am always thankful for her positive encouragement and praise.

show abstract

Section: Introductionmentioning

confidence: 64%

Towards More Robust Natural Language Understanding

Zhang¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The ĉ is obtained by one convolution filter along with maximum pooling layer, and a feature sequence obtained with t convolution filters is shown in (4).…”

Section: B Cnn-attention Based Answer Selectormentioning

confidence: 99%

“…We automatically prepared the domain-specific corpus for "masked Language Model" and "next sentence prediction" to generate the data for pretraining on each domain. We utilized the NodeBox English library, which has been succeeded by the Pattern Python library 4 , for analyzing the keyword's role and expansion term transformation.…”

Section: B Data Pre-processing and Experimental Settingsmentioning

confidence: 99%

“…The focus of this paper is closed-domain sentence-level QA [4,5,6,7]. This is an important and challenging field to study because many problems could be addressed by building domain-specific QA systems.…”

Section: Introductionmentioning

confidence: 99%

“…The language models have achieved nearhuman, or even better performance, on popular open-domain QA tasks such as SQuAD [11]. Despite this progress in open-domain QA, existing models for closed-domain QA [4,5,6,7,12] are comparatively less effective and opendomain QA models do not perform as expected for domainspecific questions. Our goal in this paper is to develop a closed-domain QA system that can be easily adapted to different domains with only a small training data set.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion

et al. 2022

View full text Add to dashboard Cite

In closed-domain Question Answering (QA), the goal is to retrieve answers to questions within a specific domain. The main challenge of closed-domain QA is to develop a model that only requires small datasets for training since large-scale corpora may not be available. One approach is a flexible QA model that can adapt to different closed domains and train on their corpora. In this paper, we present a novel versatile reading comprehension style approach for closed-domain QA (called CA-AcdQA). The approach is based on pre-trained contextualized language models, Convolutional Neural Network (CNN), and a self-attention mechanism. The model captures the relevance between the question and context sentences at different levels of granularity by exploring the dependencies between the features extracted by the CNN. Moreover, we include candidate answer identification and question expansion techniques for context reduction and rewriting ambiguous questions. The model can be tuned to different domains with a small training dataset for sentence-level QA. The approach is tested on four publicly-available closed-domain QA datasets: Tesla (person), California (region), EU-law (system), and COVID-QA (biomedical) against nine other QA approaches. Results show that the ALBERT model variant outperforms all approaches on all datasets with a significant increase in Exact Match and F1 score. Furthermore, for the Covid-19 QA in which the text is complicated and specialized, the model is improved considerably with additional biomedical training resources (an F1 increase of 15.9 over the next highest baseline).

show abstract

Entity-Enriched Neural Models for Clinical Question Answering

Cited by 2 publications

References 0 publications

Towards More Robust Natural Language Understanding

Towards More Robust Natural Language Understanding

Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion

Contact Info

Product

Resources

About