Search Results for author:
Found 7 papers, 5 papers with code
A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling
The joint model for the two tasks is becoming a tendency in SLU.
Ranked #6 on
Intent Detection
on SNIPS
RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion
Since RTFE learns each timestamp recursively, it can naturally transit to future timestamps.
KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD
With the introduction of a priori knowledge of 10 lesion signs of input images into the KFWC, we aim to accelerate the KFWC by means of multi-label classification pre-training, to locate the decisive image features in the fine-grained disease classification task and therefore achieve better classification.
HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level
The global-level attention can model the graphical structure of HKG using hypergraph dual-attention layers, while the local-level attention can learn the sequential structure inside H-Facts via heterogeneous self-attention layers.
Ranked #1 on
Link Prediction
on Wikipeople
Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction
To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction.
Event-based N-ary Relaiton Extraction
Hypergraph-based N-ary Relaiton Extraction
+3
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models
Knowledge Base Question Answering (KBQA) aims to derive answers to natural language questions over large-scale knowledge bases (KBs), which are generally divided into two research components: knowledge retrieval and semantic parsing.
Ranked #1 on
Knowledge Base Question Answering
on WebQuestionsSP
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation
In order to address this problem, we present Augmentation-free Dense Contrastive Knowledge Distillation (Af-DCD), a new contrastive distillation learning paradigm to train compact and accurate deep neural networks for semantic segmentation applications.
