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Found 17 papers, 9 papers with code
End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning
To tackle these shortcomings, we propose two joint frameworks for ECPE: 1) multi-label learning for the extraction of the cause clauses corresponding to the specified emotion clause (CMLL) and 2) multi-label learning for the extraction of the emotion clauses corresponding to the specified cause clause (EMLL).
Ranked #3 on
Emotion-Cause Pair Extraction
on ECPE
Accelerating Large Batch Training via Gradient Signal to Noise Ratio (GSNR)
As models for nature language processing (NLP), computer vision (CV) and recommendation systems (RS) require surging computation, a large number of GPUs/TPUs are paralleled as a large batch (LB) to improve training throughput.
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems.
Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study
Recently, ChatGPT has drawn great attention from both the research community and the public.
SKDBERT: Compressing BERT via Stochastic Knowledge Distillation
In this paper, we propose Stochastic Knowledge Distillation (SKD) to obtain compact BERT-style language model dubbed SKDBERT.
BViT: Broad Attention based Vision Transformer
In this paper, we propose the broad attention to improve the performance by incorporating the attention relationship of different layers for vision transformer, which is called BViT.
Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search
Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology.
Multimodal Emotion-Cause Pair Extraction in Conversations
It is also interesting to discover emotions and their causes in conversations.
ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search
Compared with the traditional rule-based pruning method, this pipeline saves human labor and achieves a higher compression ratio with lower accuracy loss.
Heuristic Rank Selection with Progressively Searching Tensor Ring Network
Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region.
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search
For this consequent issue, two solutions are given: 1) we propose Confident Learning Rate (CLR) that considers the confidence of gradient for architecture weights update, increasing with the training time of over-parameterized BCNN; 2) we introduce the combination of partial channel connections and edge normalization that also can improve the memory efficiency further.
ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction
In recent years, a new interesting task, called emotion-cause pair extraction (ECPE), has emerged in the area of text emotion analysis.
Ranked #8 on
Emotion-Cause Pair Extraction
on ECPE
BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture
In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue.
RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction
The emotion cause extraction (ECE) task aims at discovering the potential causes behind a certain emotion expression in a document.
Ranked #6 on
Emotion Cause Extraction
on ECE
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts
Emotion cause extraction (ECE), the task aimed at extracting the potential causes behind certain emotions in text, has gained much attention in recent years due to its wide applications.
Ranked #13 on
Emotion-Cause Pair Extraction
on ECPE
From Independent Prediction to Re-ordered Prediction: Integrating Relative Position and Global Label Information to Emotion Cause Identification
We introduce a relative position augmented embedding learning algorithm, and transform the task from an independent prediction problem to a reordered prediction problem, where the dynamic global label information is incorporated.
Ranked #7 on
Emotion Cause Extraction
on ECE
Densely Connected Bidirectional LSTM with Applications to Sentence Classification
Deep neural networks have recently been shown to achieve highly competitive performance in many computer vision tasks due to their abilities of exploring in a much larger hypothesis space.
