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Found 8 papers, 5 papers with code
ICON: Improving Inter-Report Consistency of Radiology Report Generation via Lesion-aware Mix-up Augmentation
Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated reports.
Medical Dialogue Generation via Intuitive-then-Analytical Differential Diagnosis
Clinicians typically employ both intuitive and analytic reasoning to formulate a differential diagnosis.
RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning
It then combines the historical records, spatiotemporal information, and radiographs for report generation, where a disease progression graph and dynamic progression reasoning mechanism are devised to accurately select the attributes of each observation and progression.
ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning
This paper explores the task of radiology report generation, which aims at generating free-text descriptions for a set of radiographs.
Medical Dialogue Generation via Dual Flow Modeling
It extracts the medical entities and dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow, respectively.
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network
Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages.
Ranked #6 on
Emotion-Cause Pair Extraction
on ECPE
CAUnLP at NLP4IF 2019 Shared Task: Context-Dependent BERT for Sentence-Level Propaganda Detection
This paper presents the sys- tem of our participation in the sentence-level subtask of the propaganda detection shared task.
Joint Learning for Emotion Classification and Emotion Cause Detection
We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis.
