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Found 5 papers, 1 papers with code
MODE-LSTM: A Parameter-efficient Recurrent Network with Multi-Scale for Sentence Classification
The central problem of sentence classification is to extract multi-scale n-gram features for understanding the semantic meaning of sentences.
CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning
Metaphors are ubiquitous in natural language, and detecting them requires contextual reasoning about whether a semantic incongruence actually exists.
A Survey on Post-training of Large Language Models
The emergence of Large Language Models (LLMs) has fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration.
A Span-based Dynamic Local Attention Model for Sequential Sentence Classification
Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear.
Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification
We then introduce a joint embedding loss and a matching learning loss to model the matching relationship between the text semantics and the label semantics.
