An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking (D18-1)
Copied to clipboard
| Challenge: | Experimental results show that the extracted emotion-associated topic words represent emotion-evoking events. |
| Approach: | They propose an interpretable neural network approach for relevant emotion ranking . they initialize the hidden layer to approximate the behavior of topic models . |
| Outcome: | The proposed approach performs better than state-of-the-art methods on real-world corpora. |
Similar Papers
Interpretable Relevant Emotion Ranking with Event-Driven Attention (D19-1)
Copied to clipboard
| Challenge: | Existing studies ignore the latent event information in documents . Existing methods for detecting emotions are limited to a few words . |
| Approach: | They propose to integrate event information into a deep learning architecture to extract relevant emotion ranking models using corpus-level event embeddings and document-level events. |
| Outcome: | The proposed model performs better than state-of-the-art emotion detection and multi-label approaches on three real-world corpora and interpretable results shed light on the events which trigger certain emotions. |
Relevant Emotion Ranking from Text Constrained with Emotion Relationships (N18-1)
Copied to clipboard
| Challenge: | Existing methods to detect emotions from text are lexicon-based and learning-based . experimental results show that the proposed framework is better than state-of-the-art methods . |
| Approach: | They propose to use a multi-label classification problem to generate a ranked list of relevant emotions. |
| Outcome: | The proposed framework performs better than state-of-the-art methods and multi-label learning methods on two real-world corpora. |
Adversarial Attention Modeling for Multi-dimensional Emotion Regression (P19-1)
Copied to clipboard
| Challenge: | Empirical evaluation on EMOBANK corpus shows that our approach achieves notable improvements in r-values over the state-of-the-art baselines. |
| Approach: | They propose a neural network-based approach to multi-dimensional emotion regression which automatically rates multiple emotion dimension scores for an input text. |
| Outcome: | The proposed approach achieves notable improvements in r-values on both EMOBANK Reader’s and Writer’s multi-dimensional emotion regression tasks over the state-of-the-art baselines. |
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to emotion detection are lexicon-based, graphical model-based and linear classifier-based. |
| Approach: | They propose a transfer learning architecture to divide sentence representation into two different feature spaces which capture general sentiment words and other important emotion-specific words via a dual attention mechanism. |
| Outcome: | The proposed model can capture general sentiment words and other emotion-specific words via a dual attention mechanism on two benchmark datasets. |
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs (N19-1)
Copied to clipboard
| Challenge: | Existing methods to categorize sentiments and emotions in text are limited. |
| Approach: | They propose to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. |
| Outcome: | The proposed method improves on a recently published dataset for categorizing human needs. |
Affection Driven Neural Networks for Sentiment Analysis (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing deep neural network models lack mechanisms to highlight important sentiment terms. |
| Approach: | They propose a method to incorporate affective knowledge into deep neural network models by mapping affective influence vectors to an affective impact value and integrating them into long-term memory models to highlight affective terms. |
| Outcome: | The proposed approach improves on three large datasets by 1.0% to 1.5% on the benchmark datasets. |
Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification (2024.emnlp-main)
Copied to clipboard
| Challenge: | Emotion classification is an important task with applications in education, virtual reality, and robotics. |
| Approach: | They propose to use token embeddings to generate a "semantic-anchor graph" using semantic anchors, sentences can be projected onto them to form a graph . |
| Outcome: | Empirically, the proposed system can generate meaningful semantic anchors and discriminative graph patterns for different emotion. |
A Hierarchical Neural Attention-based Text Classifier (D18-1)
Copied to clipboard
| Challenge: | Existing hierarchical classification models are unable to handle large corpora and the number of categories increases with increasing corpus. |
| Approach: | They propose to use external knowledge to introduce a hierarchical neural attention-based classifier to help with the classification of documents. |
| Outcome: | The proposed model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability. |
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis (D19-1)
Copied to clipboard
| Challenge: | Multi-modal analysis is a field emerging in the fields of natural language processing, computer vision and speech processing . multimodal analysis uses a variety of information from multiple sources to build efficient systems . acoustic and visual information can provide better information for classification decisions . |
| Approach: | They propose a recurrent neural network based approach for multi-modal sentiment and emotion analysis . they employ a context-aware attention module to exploit the correspondence among neighboring utterances . |
| Outcome: | The proposed model learns inter-modal interaction among participating modalities through auto-encoder mechanism . it is compared with existing state-of-the-art models on five standard multi-modal affect analysis datasets . |
Transformation Networks for Target-Oriented Sentiment Classification (P18-1)
Copied to clipboard
| Challenge: | a new model for sentiment classification uses attention instead of attention to classify sentiment polarities over individual opinion targets. |
| Approach: | They propose a model that uses a CNN layer to extract salient features from transformed word representations from a bi-directional RNN layer. |
| Outcome: | The proposed model achieves state-of-the-art on a few benchmarks. |