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Found 13 papers, 3 papers with code
Radiology-Aware Model-Based Evaluation Metric for Report Generation
Furthermore, we demonstrate that one of our checkpoints exhibits a high correlation with human judgment, as assessed using the publicly available annotations of six board-certified radiologists, using a set of 200 reports.
Boosting Radiology Report Generation by Infusing Comparison Prior
To tackle this issue, we propose a novel approach that leverages a rule-based labeler to extract comparison prior information from radiology reports.
Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III
Methods: We present an extensive evaluation of the impact of different federation and differential privacy techniques when training models on the open-source MIMIC-III dataset.
Improving the Cross-Lingual Generalisation in Visual Question Answering
While several benefits were realized for multilingual vision-language pretrained models, recent benchmarks across various tasks and languages showed poor cross-lingual generalisation when multilingually pre-trained vision-language models are applied to non-English data, with a large gap between (supervised) English performance and (zero-shot) cross-lingual transfer.
Progressive Transformer-Based Generation of Radiology Reports
Inspired by Curriculum Learning, we propose a consecutive (i. e., image-to-text-to-text) generation framework where we divide the problem of radiology report generation into two steps.
Low-Resource Adaptation of Neural NLP Models
Real-world applications of natural language processing (NLP) are challenging.
Zero-Shot Cross-Lingual Transfer with Meta Learning
We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first.
Reinforcement-based denoising of distantly supervised NER with partial annotation
The outcome of distant supervision for NER, however, is often noisy.
SIRIUS-LTG: An Entity Linking Approach to Fact Extraction and Verification
The experiments show that the pipeline with simple Cosine Similarity using TFIDF in sentence selection along with DA model as labelling model achieves the best results on the development set (F1 evidence: 32. 17, label accuracy: 59. 61 and FEVER score: 0. 3778).
Syntactic Dependency Representations in Neural Relation Classification
We investigate the use of different syntactic dependency representations in a neural relation classification task and compare the CoNLL, Stanford Basic and Universal Dependencies schemes.
SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers
This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers.
