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Found 16 papers, 8 papers with code
Minimize Quantization Output Error with Bias Compensation
Quantization is a promising method that reduces memory usage and computational intensity of Deep Neural Networks (DNNs), but it often leads to significant output error that hinder model deployment.
ToMBench: Benchmarking Theory of Mind in Large Language Models
Theory of Mind (ToM) is the cognitive capability to perceive and ascribe mental states to oneself and others.
Controlled Text Generation for Large Language Model with Dynamic Attribute Graphs
In this study, we introduce a pluggable CTG framework for Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled text generation (DATG).
LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty
Named Entity Recognition (NER) serves as a fundamental task in natural language understanding, bearing direct implications for web content analysis, search engines, and information retrieval systems.
Coreference Graph Guidance for Mind-Map Generation
Then we employ a coreference graph encoder to mine the potential governing relations between sentences.
Uncertainty in Natural Language Processing: Sources, Quantification, and Applications
Therefore, in this survey, we provide a comprehensive review of uncertainty-relevant works in the NLP field.
Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction
We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens.
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments.
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation
By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-of-the-art methods significantly in low-resource settings.
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on ACOS
Classical Sequence Match is a Competitive Few-Shot One-Class Learner
Nowadays, transformer-based models gradually become the default choice for artificial intelligence pioneers.
Efficient Mind-Map Generation via Sequence-to-Graph and Reinforced Graph Refinement
A mind-map is a diagram that represents the central concept and key ideas in a hierarchical way.
Multi-Label Few-Shot Learning for Aspect Category Detection
Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence.
Hierarchical Ranking for Answer Selection
Answer selection is a task to choose the positive answers from a pool of candidate answers for a given question.
Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis
The attention is expected to concentrate on opinion words for accurate sentiment prediction.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification
Cross-domain sentiment classification has drawn much attention in recent years.
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis
Aspect level sentiment classification is a fine-grained sentiment analysis task.
