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Found 12 papers, 5 papers with code
Learning causal graphs using variable grouping according to ancestral relationship
However, when the sample size is small relative to the number of variables, the accuracy of estimating causal graphs using existing methods decreases.
From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction
In this paper, we introduce a novel From Graph to Word Bag (FWGB) approach, which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge's reasoning process.
Enhancing Court View Generation with Knowledge Injection and Guidance
Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions.
Enhancing Language Representation with Constructional Information for Natural Language Understanding
We adopt usage-based construction grammar as the basis of our work, which is highly compatible with statistical models such as PLMs.
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction
Grammatical Error Correction (GEC) has been broadly applied in automatic correction and proofreading system recently.
Ranked #1 on
Grammatical Error Correction
on FCGEC
Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks
Aspect-level sentiment analysis aims to determine the sentiment polarity towards a specific target in a sentence.
Ranked #1 on
Aspect-Based Sentiment Analysis (ABSA)
on Rest16
Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
Our Graph- PGCR module is plug-and-play, which can be integrated into any architecture to improve its performance.
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase.
Reconstruct Locally, Localize Globally: A Model Free Method for Object Pose Estimation
If a CAD model is not available, it is possible to use multi-view visual reconstruction methods to create a geometric model, and use this in the same manner.
Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette
3D shape reconstruction from a single image is a highly ill-posed problem.
Attentive Sequence to Sequence Translation for Localizing Clips of Interest by Natural Language Descriptions
We validate the effectiveness of our ASST on two large-scale datasets.
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image
Detecting objects and their 6D poses from only RGB images is an important task for many robotic applications.
