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Found 15 papers, 12 papers with code
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification
Target-oriented multimodal sentiment classification (TMSC) is a new subtask of aspect-based sentiment analysis, which aims to determine the sentiment polarity of the opinion target mentioned in a (sentence, image) pair.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Knowledge-aware Dual-side Attribute-enhanced Recommendation
Specifically, we build \textit{user preference representations} and \textit{attribute fusion representations} upon the attribute information in knowledge graphs, which are utilized to enhance \textit{collaborative filtering} (CF) based user and item representations, respectively.
Cobra Effect in Reference-Free Image Captioning Metrics
If a certain metric has flaws, it will be exploited by the model and reflected in the generated sentences.
EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models
Multimodal large language models (MLLMs) have attracted increasing attention in the past few years, but they may still generate descriptions that include objects not present in the corresponding images, a phenomenon known as object hallucination.
M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis
Therefore, in this work, we focus on whether the negative impact of noisy images can be reduced without modifying the data.
DRIN: Dynamic Relation Interactive Network for Multimodal Entity Linking
Multimodal Entity Linking (MEL) is a task that aims to link ambiguous mentions within multimodal contexts to referential entities in a multimodal knowledge base.
Dynamic Demonstrations Controller for In-Context Learning
Previous studies have revealed that ICL is sensitive to the selection and the ordering of demonstrations.
Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction
In this paper, we introduce a new dataset, named DMASTE, which is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews for the task.
Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category Detection
Multi-Label Few-Shot Aspect Category Detection (FS-ACD) is a new sub-task of aspect-based sentiment analysis, which aims to detect aspect categories accurately with limited training instances.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
Cost-Sensitive Active Learning for Incomplete Data
A reasonable machine learning scenario involves obtaining certain values and labels at cost on request.
Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning
Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results.
Attention Transfer Network for Aspect-level Sentiment Classification
In neural network-based methods for ASC, most works employ the attention mechanism to capture the corresponding sentiment words of the opinion target, then aggregate them as evidence to infer the sentiment of the target.
Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction
To validate the feasibility and compatibility of GTS, we implement three different GTS models respectively based on CNN, BiLSTM, and BERT, and conduct experiments on the aspect-oriented opinion pair extraction and opinion triplet extraction datasets.
Ranked #1 on
Aspect Sentiment Triplet Extraction
on Res14
Aspect-Sentiment-Opinion Triplet Extraction
Aspect Sentiment Triplet Extraction
Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction
In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE.
Aspect-oriented Opinion Extraction
General Classification
+3
Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation
In the spectrogram of a mixture of singing voices and music signals, there is usually more information about the voice in the low frequency bands than the high frequency bands.
