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Found 14 papers, 4 papers with code
UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization
We show that both transfer learning methods combined achieve the highest ROUGE scores.
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization
We introduce a deep reinforcement learning approach to topic-focused abstractive summarization, trained on rewards with a novel negative example baseline.
Joint Summarization-Entailment Optimization for Consumer Health Question Understanding
Understanding the intent of medical questions asked by patients, or Consumer Health Questions, is an essential skill for medical Conversational AI systems.
Putting Figures on Influences on Moroccan Darija from Arabic, French and Spanish using the WordNet
Moroccan Darija is a variant of Arabic with many influences.
Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters
Our MLM filter can generalize to different models and tasks, and be used as a drop-in replacement for CLIPScore.
InfiMM-Eval: Complex Open-Ended Reasoning Evaluation For Multi-Modal Large Language Models
To mitigate this issue, we manually curate a benchmark dataset specifically designed for MLLMs, with a focus on complex reasoning tasks.
Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision
The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document.
Sentence-level Privacy for Document Embeddings
User language data can contain highly sensitive personal content.
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem
Without the use of a knowledge base or candidate sets, our model sets a new state of the art in two benchmark datasets of entity linking: COMETA in the biomedical domain, and AIDA-CoNLL in the news domain.
A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding
Users of medical question answering systems often submit long and detailed questions, making it hard to achieve high recall in answer retrieval.
Recursive Tree-Structured Self-Attention for Answer Sentence Selection
The recursive nature of our model is able to represent all levels of syntactic parse trees with only one additional self-attention layer.
Rethinking Self-Attention: Towards Interpretability in Neural Parsing
Finally, we find that the Label Attention heads learn relations between syntactic categories and show pathways to analyze errors.
Ranked #1 on
Dependency Parsing
on Penn Treebank
Interpretable Structure-aware Document Encoders with Hierarchical Attention
We show our model's interpretability by visualizing how our model distributes attention inside a document.
Towards Producing Human-Validated Translation Resources for the Fula language through WordNet Linking
We propose methods to link automatically parsed linguistic data to the WordNet.
