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Found 19 papers, 8 papers with code
KU_ED at SocialDisNER: Extracting Disease Mentions in Tweets Written in Spanish
This paper describes our system developed for the Social Media Mining for Health (SMM4H) 2022 SocialDisNER task.
Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks
While recent advancements in commercial large language models (LM) have shown promising results in medical tasks, their closed-source nature poses significant privacy and security concerns, hindering their widespread use in the medical field.
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing
Contrastive language-image pre-training (CLIP) models have demonstrated considerable success across various vision-language tasks, such as text-to-image retrieval, where the model is required to effectively process natural language input to produce an accurate visual output.
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain.
LIQUID: A Framework for List Question Answering Dataset Generation
Question answering (QA) models often rely on large-scale training datasets, which necessitates the development of a data generation framework to reduce the cost of manual annotations.
Ranked #1 on
Question Answering
on MultiSpanQA
Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations
Most weakly supervised named entity recognition (NER) models rely on domain-specific dictionaries provided by experts.
Simple Questions Generate Named Entity Recognition Datasets
Recent named entity recognition (NER) models often rely on human-annotated datasets, requiring the significant engagement of professional knowledge on the target domain and entities.
Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles
This paper is a technical report on our system submitted to the chemical identification task of the BioCreative VII Track 2 challenge.
Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering
One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis.
``Killing Me'' Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites.
"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites.
How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?
The number of biomedical literature on new biomedical concepts is rapidly increasing, which necessitates a reliable biomedical named entity recognition (BioNER) model for identifying new and unseen entity mentions.
Look at the First Sentence: Position Bias in Question Answering
In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e. g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions.
Exploring The Spatial Reasoning Ability of Neural Models in Human IQ Tests
Also, we analyze how neural models solve spatial reasoning tests with visual aids.
Fast frequency discrimination and phoneme recognition using a biomimetic membrane coupled to a neural network
When excited by sound, this membrane responds with a frequency-dependent displacement pattern that is detected and identified by the auditory hair cells combined with the human neural system.
Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network
Second, they assumed that all transactions are equally important in predicting demographic attributes.
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering
Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source.
Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector
We present a focal liver lesion detection model leveraged by custom-designed multi-phase computed tomography (CT) volumes, which reflects real-world clinical lesion detection practice using a Single Shot MultiBox Detector (SSD).
Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets
Graphs provide a powerful means for representing complex interactions between entities.
