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Found 21 papers, 11 papers with code
Neural CRF Model for Sentence Alignment in Text Simplification
The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles.
Ranked #1 on
Text Simplification
on Newsela
Universal Machine Learning Kohn-Sham Hamiltonian for Materials
By offering a reliable efficient framework for computing electronic properties, this universal Hamiltonian model lays the groundwork for advancements in diverse fields, such as easily providing a huge data set of electronic structures and also making the materials design across the whole periodic table possible.
Modeling Intensification for Sign Language Generation: A Computational Approach
To employ our strategies, we first annotate a subset of the benchmark PHOENIX-14T, a German Sign Language dataset, with different levels of intensification.
General time-reversal equivariant neural network potential for magnetic materials
This study introduces time-reversal E(3)-equivariant neural network and SpinGNN++ framework for constructing a comprehensive interatomic potential for magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic moments.
Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining
This paper presents an overview of the ImageArg shared task, the first multimodal Argument Mining shared task co-located with the 10th Workshop on Argument Mining at EMNLP 2023.
WIKIBIAS: Detecting Multi-Span Subjective Biases in Language
Biases continue to be prevalent in modern text and media, especially subjective bias – a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth.
Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions
Though many algorithms can be used to automatically summarize legal case decisions, most fail to incorporate domain knowledge about how important sentences in a legal decision relate to a representation of its document structure.
Extractive Summarization
Unsupervised Extractive Summarization
ReflectSumm: A Benchmark for Course Reflection Summarization
This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students' reflective writing.
Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking
We propose a simple approach for the abstractive summarization of long legal opinions that considers the argument structure of the document.
STRONG -- Structure Controllable Legal Opinion Summary Generation
We propose an approach for the structure controllable summarization of long legal opinions that considers the argument structure of the document.
Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild
Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world.
Face Attribute Prediction Using Off-the-Shelf CNN Features
Predicting attributes from face images in the wild is a challenging computer vision problem.
Leveraging the Power of Gabor Phase for Face Identification: A Block Matching Approach
Different from face verification, face identification is much more demanding.
Target Aware Network Adaptation for Efficient Representation Learning
This paper presents an automatic network adaptation method that finds a ConvNet structure well-suited to a given target task, e. g., image classification, for efficiency as well as accuracy in transfer learning.
Regularizing CNN Transfer Learning with Randomised Regression
That is, a CNN is efficiently regularized without additional resources of data or prior domain expertise.
Discourse Level Factors for Sentence Deletion in Text Simplification
We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected.
Edge-based Tensor prediction via graph neural networks
Message-passing neural networks (MPNN) have shown extremely high efficiency and accuracy in predicting the physical properties of molecules and crystals, and are expected to become the next-generation material simulation tool after the density functional theory (DFT).
Spin-Dependent Graph Neural Network Potential for Magnetic Materials
The development of machine learning interatomic potentials has immensely contributed to the accuracy of simulations of molecules and crystals.
A Machine Learning Generative Method for Automating Antenna Design and Optimization
For a dual resonance antenna design with wide bandwidth, our proposed method is in par with Trust Region Framework and much better than the other mature machine learning algorithms including the widely used Genetic Algorithm and Particle Swarm Optimization.
Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids
Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations.
Image Classifier Based Generative Method for Planar Antenna Design
To extend the antenna design on printed circuit boards (PCBs) for more engineers of interest, we propose a simple method that models PCB antennas with a few basic components.
