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Found 17 papers, 11 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.
Towards Multi-label Unknown Intent Detection
In this paper, we propose a more desirable task, multi-label unknown intent detection, to detect whether the utterance contains the unknown intent, in which each utterance may contain multiple intents.
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 Category Detection
+1
Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction
Limited labeled data increase the risk of distribution shift between test data and training data.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Based on the analysis, we hence propose FreeMatch to define and adjust the confidence threshold in a self-adaptive manner according to the model's learning status.
Continuous-time mean-variance portfolio selection under non-Markovian regime-switching model with random horizon
All the results are different from those in the problem with fixed time horizon.
Margin Calibration for Long-Tailed Visual Recognition
The long-tailed class distribution in visual recognition tasks poses great challenges for neural networks on how to handle the biased predictions between head and tail classes, i. e., the model tends to classify tail classes as head classes.
UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost
Therefore, in this paper, we integrate different dropout techniques into the training of Transformer models.
Ranked #3 on
Machine Translation
on IWSLT2014 English-German
Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification
In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE to achieve the goal of facilitating them simultaneously.
Transformer-based Multi-Aspect Modeling for Multi-Aspect Multi-Sentiment Analysis
To facilitate the research of ABSA, NLPCC 2020 Shared Task 2 releases a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset.
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
Dynamic Programming Principle for Backward Doubly Stochastic Recursive Optimal Control Problem and Sobolev Weak Solution of The Stochastic Hamilton-Bellman Equation
In this paper, we study backward doubly stochastic recursive optimal control problem where the cost function is described by the solution of a backward doubly stochastic differential equation.
Probability Optimization and Control
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
+1
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling
In this paper, we propose a novel sequence labeling subtask for ABSA named TOWE (Target-oriented Opinion Words Extraction), which aims at extracting the corresponding opinion words for a given opinion target.
Aspect-oriented Opinion Extraction
target-oriented opinion words extraction
Improving Review Representations with User Attention and Product Attention for Sentiment Classification
Recently, some works achieved improvement by incorporating user and product information to generate a review representation.
Ranked #3 on
Sentiment Analysis
on User and product information
