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TitleStatusHype
Edinburgh Clinical NLP at MEDIQA-CORR 2024: Guiding Large Language Models with Hints—0
EdiT5: Semi-Autoregressive Text-Editing with T5 Warm-Start—0
Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modelling—0
A Deep Neural Network Approach To Parallel Sentence Extraction—0
Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models—0
A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU—0
Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models—0
EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG—0
Effect Generation Based on Causal Reasoning—0
Relation Classification with Entity Type Restriction—0
A Syntax-Aware Edit-based System for Text Simplification—0
Effective Attention Modeling for Aspect-Level Sentiment Classification—0
Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism—0
A Syntactic Approach to Domain-Specific Automatic Question Generation—0
Effective Context and Fragment Feature Usage for Named Entity Recognition—0
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction—0
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue—0
Effective Data Augmentation for Sentence Classification Using One VAE per Class—0
Relation extraction between the clinical entities based on the shortest dependency path based LSTM—0
Effective FAQ Retrieval and Question Matching With Unsupervised Knowledge Injection—0
Effective General-Domain Data Inclusion for the Machine Translation Task by Vanilla Transformers—0
Relation Extraction using Explicit Context Conditioning—0
Asynchronous Deep Interaction Network for Natural Language Inference—0
Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data—0
Relation Extraction with Explanation—0
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