Search Results for author:
Found 13 papers, 6 papers with code
Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition
General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city.
ItD: Large Language Models Can Teach Themselves Induction through Deduction
Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited ability to conduct induction.
HYTREL: Hypergraph-enhanced Tabular Data Representation Learning
Language models pretrained on large collections of tabular data have demonstrated their effectiveness in several downstream tasks.
ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion
However, there has been limited research on the zero-shot KBC settings, where we need to deal with unseen entities and relations that emerge in a constantly growing knowledge base.
DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation
Experimental results on $4, 773$ dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.
TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes
Experiments show that the algorithms trained on TO-Scene indeed work on the realistic test data, and our proposed tabletop-aware learning strategy greatly improves the state-of-the-art results on both 3D semantic segmentation and object detection tasks.
DArch: Dental Arch Prior-Assisted 3D Tooth Instance Segmentation With Weak Annotations
Experimental results on 4, 773 dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.
Few-Shot Incremental Learning for Label-to-Image Translation
When given new training samples annotated with novel semantic classes, the models should be trained from scratch with both learned and new classes.
Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition
Named entity recognition (NER) is well studied for the general domain, and recent systems have achieved human-level performance for identifying common entity types.
Spatiotemporal information conversion machine for time-series prediction
Making predictions in a robust way is a difficult task only based on the observed data of a nonlinear system.
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents
This paper proposes a new task regarding event reason extraction from document-level texts.
Reconstructing Event Regions for Event Extraction via Graph Attention Networks
Event information is usually scattered across multiple sentences within a document.
DEFM: Delay E mbedding based Forecast Machine for Time Series Forecasting by Spatiotemporal Information Transformation
Making accurate forecasts for a complex system is a challenge in various practical applications.
