Search Results for author:
Found 6 papers, 4 papers with code
Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders
Abstractive summarization can generate high quality results with the development of the neural network.
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation
Low-Rank Adaptation (LoRA) introduces auxiliary parameters for each layer to fine-tune the pre-trained model under limited computing resources.
Chinese Fine-Grained Financial Sentiment Analysis with Large Language Models
Entity-level fine-grained sentiment analysis in the financial domain is a crucial subtask of sentiment analysis and currently faces numerous challenges.
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing
Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences.
Ranked #5 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
Dialog Relation Extraction
Document-level Relation Extraction
+2
Discriminative Reasoning for Document-level Relation Extraction
Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i. e., pattern recognition, logical reasoning, coreference reasoning, etc.)
Ranked #24 on
Relation Extraction
on DocRED
Document-Level Relation Extraction with Reconstruction
In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years.
Ranked #35 on
Relation Extraction
on DocRED
