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Found 10 papers, 4 papers with code
Inferring missing metadata from environmental policy texts
The National Environmental Policy Act (NEPA) provides a trove of data on how environmental policy decisions have been made in the United States over the last 50 years.
Pyramid Real Image Denoising Network
Second, at the multi-scale denoising stage, pyramid pooling is utilized to extract multi-scale features.
Ranked #2 on
Color Image Denoising
on Darmstadt Noise Dataset
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
How does BERT's attention change when you fine-tune? An analysis methodology and a case study in negation scope
We apply this methodology to test BERT and RoBERTa on a hypothesis that some attention heads will consistently attend from a word in negation scope to the negation cue.
Using the Hammer Only on Nails: A Hybrid Method for Evidence Retrieval for Question Answering
Evidence retrieval is a key component of explainable question answering (QA).
SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing
Participants are then tested on data representing a new (target) domain.
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation
This paper describes our systems for negation detection and time expression recognition in SemEval 2021 Task 10, Source-Free Domain Adaptation for Semantic Processing.
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing
In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data.
A Comparison of Strategies for Source-Free Domain Adaptation
Data sharing restrictions are common in NLP, especially in the clinical domain, but there is limited research on adapting models to new domains without access to the original training data, a setting known as source-free domain adaptation.
Do pretrained transformers infer telicity like humans?
Pretrained transformer-based language models achieve state-of-the-art performance in many NLP tasks, but it is an open question whether the knowledge acquired by the models during pretraining resembles the linguistic knowledge of humans.
