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Found 11 papers, 3 papers with code
Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
Recently graph-based methods have been adopted for Abstractive Text Summarization.
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization
In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task.
Knowledge-Aware Neuron Interpretation for Scene Classification
Specifically, for concept completeness, we present core concepts of a scene based on knowledge graph, ConceptNet, to gauge the completeness of concepts.
MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation
Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-event relationships.
A Comprehensive and Reliable Feature Attribution Method: Double-sided Remove and Reconstruct (DoRaR)
While avoiding the artifacts problem, this new category suffers from the Encoding Prediction in the Explanation (EPITE) problem, in which the predictor's decisions rely not on the features, but on the masks that selects those features.
KoLA: Carefully Benchmarking World Knowledge of Large Language Models
The unprecedented performance of large language models (LLMs) necessitates improvements in evaluations.
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension
Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding.
Machine Reading Comprehension
Natural Language Understanding
A Frame-based Sentence Representation for Machine Reading Comprehension
Sentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC).
Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning
Particularly, the U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images.
Unsupervised Trajectory Segmentation and Promoting of Multi-Modal Surgical Demonstrations
To improve the efficiency of surgical trajectory segmentation for robot learning in robot-assisted minimally invasive surgery, this paper presents a fast unsupervised method using video and kinematic data, followed by a promoting procedure to address the over-segmentation issue.
