Search Results for author:
Found 12 papers, 8 papers with code
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models
KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding.
CIDR: A Cooperative Integrated Dynamic Refining Method for Minimal Feature Removal Problem
The minimal feature removal problem in the post-hoc explanation area aims to identify the minimal feature set (MFS).
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding
In the experiments, we evaluate KANGAROO over various knowledge-aware and general NLP tasks in both full and few-shot learning settings, outperforming various KEPLM training paradigms performance in closed-domains significantly.
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
Reasoning is a distinctive human capacity, enabling us to address complex problems by breaking them down into a series of manageable cognitive steps.
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding
In recent years, vision language pre-training frameworks have made significant progress in natural language processing and computer vision, achieving remarkable performance improvement on various downstream tasks.
Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training
Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis.
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs
As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e. g., Graph Neural Networks (GNNs).
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing
The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP).
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction
In this paper, we propose a hierarchical contrastive learning Framework for Distantly Supervised relation extraction (HiCLRE) to reduce noisy sentences, which integrate the global structural information and local fine-grained interaction.
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities.
SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining
Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding.
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources
In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving.
