https://github.com/wzh-nlp/www25-cmas
공식 코드A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition
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A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding
Revisiting non-English Text Simplification: A Unified Multilingual Benchmark
Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator
Improving Contrastive Learning of Sentence Embeddings from AI Feedback
Rethinking the Reasonability of the Test Set for Simultaneous Machine Translation
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning
Controlling Neural Machine Translation Formality with Synthetic Supervision
Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection
GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding
Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia
Dynamic Global Memory for Document-level Argument Extraction
Learning to Ask: Neural Question Generation for Reading Comprehension
Language Agnostic Multilingual Information Retrieval with Contrastive Learning
Towards Making the Most of Dialogue Characteristics for Neural Chat Translation
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation
A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
Modeling Multi-Granularity Hierarchical Features for Relation Extraction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.