Graph Neural Networks (GNNs) have proven to be effective in processing a...
Time-to-event analysis, also known as survival analysis, aims to predict...
In this paper, we introduce CheXOFA, a new pre-trained vision-language m...
Determining proper quantities for ingredients is an essential part of co...
Deep learning has shown great potential in assisting radiologists in rea...
Question answering (QA) models often rely on large-scale training datase...
In order to assist the drug discovery/development process, pharmaceutica...
Named entity recognition (NER) is a fundamental part of extracting
infor...
Most weakly supervised named entity recognition (NER) models rely on
dom...
We propose a computational approach for recipe ideation, a downstream ta...
Transformer-based models have been widely used and achieved state-of-the...
Graph Neural Networks (GNNs) have been widely applied to various fields ...
Dense retrieval uses a contrastive learning framework to learn dense
rep...
In this paper, we introduce a novel framework SimSeek (simulating
inform...
Many machine translation models are trained on bilingual corpus, which
c...
In biomedical natural language processing, named entity recognition (NER...
Named entity recognition (NER) is a task of extracting named entities of...
This paper is a technical report on our system submitted to the chemical...
Pre-trained language models (LMs) have become ubiquitous in solving vari...
Despite significant interest in developing general purpose fact checking...
One of the main challenges in conversational question answering (CQA) is...
Graph Neural Networks (GNNs) have been widely applied to various fields ...
Human-Object Interaction (HOI) detection is a task of identifying "a set...
Current studies in extractive question answering (EQA) have modeled
sing...
We propose an effective consistency training framework that enforces a
t...
In Named Entity Recognition (NER), pre-trained language models have been...
Several machine learning-based spoiler detection models have been propos...
The number of biomedical literature on new biomedical concepts is rapidl...
Open-domain question answering can be reformulated as a phrase retrieval...
Deep neural networks have achieved state-of-the-art performance in a var...
Scientific novelty is important during the pandemic due to its critical ...
Generating an investment strategy using advanced deep learning methods i...
Biomedical question answering (QA) is a challenging problem due to the
s...
The recent outbreak of the novel coronavirus is wreaking havoc on the wo...
PubMed is an essential resource for the medical domain, but useful conce...
Biomedical named entities often play important roles in many biomedical ...
Many extractive question answering models are trained to predict start a...
Exposing diverse subword segmentations to neural machine translation (NM...
Although neural models have performed impressively well on various tasks...
One of the obstacles of abstractive summarization is the presence of var...
A sparse representation is known to be an effective means to encode prec...
The recent success of question answering systems is largely attributed t...
Many researchers both in academia and industry have long been interested...
With the growing importance of personalized recommendation, numerous
rec...
As a vast number of ingredients exist in the culinary world, there are
c...
Advanced methods of applying deep learning to structured data such as gr...
Most companies utilize demographic information to develop their strategy...
We applied Deep Q-Network with a Convolutional Neural Network function
a...
Biomedical text mining is becoming increasingly important as the number ...
Biomedical text mining has become more important than ever as the number...