Jointly Identifying Rhetoric and Implicit Emotions via Multi-Task Learning (2021.findings-acl)
Copied to clipboard
| Challenge: | Experimental results validate the benefit of the proposed model over the state-of-the-art baselines for rhetoric and emotion identification tasks. |
| Approach: | They propose a multi-task learning framework that can encode categorical correlation between tasks to improve rhetoric and emotion identification problem. |
| Outcome: | The proposed model can encode the categorical correlation between tasks to improve rhetoric and emotion identification problem. |
Similar Papers
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis (N19-1)
Copied to clipboard
Md Shad Akhtar, Dushyant Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
| Challenge: | Existing frameworks for sentiment and emotion analysis are not efficient for inter-task learning. |
| Approach: | They propose a multi-task learning framework that performs sentiment and emotion analysis together. |
| Outcome: | The proposed framework improves on a CMU-MOSEI dataset for sentiment and emotion analysis. |
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction (2021.findings-acl)
Copied to clipboard
Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
| Challenge: | Detecting what emotions are expressed in text is a well-studied problem in natural language processing. |
| Approach: | They propose methods that combine common-sense knowledge with multi-task learning to perform joint emotion classification and emotion cause tagging. |
| Outcome: | The proposed models improve on both tasks when using common-sense reasoning and a multitask framework. |
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem (N18-1)
Copied to clipboard
| Challenge: | a recent shift towards expressive emotion representation models has hampered deep learning in sentiment analysis. |
| Approach: | They propose a multi-task learning problem to solve a language data bottleneck . they propose to use word emotion induction as an individual task to predict emotion . |
| Outcome: | The proposed model outperforms a wide range of other methods on 9 languages and 15 conditions. |
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis (2020.acl-main)
Copied to clipboard
| Challenge: | Existing systems for sarcasm detection are limited by the use of sarcasm . sarasm is often used to convey thinly veiled disapproval humorously. |
| Approach: | They propose a multi-task deep learning framework to solve sarcasm problems simultaneously . they manually annotate a sarcsm dataset with sentiment and emotion classes . |
| Outcome: | The proposed framework is able to solve sarcasm, sentiment and emotion problems in a multi-modal conversational scenario. |
Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning (C18-1)
Copied to clipboard
| Challenge: | Sentence-level emotion detection is a challenging task due to subjectivity of emotion. |
| Approach: | They propose a model to address genre robustness in a multi-task learning problem . they use a genre-based corpus to train a neural net model with different genres . |
| Outcome: | The proposed model improves the results across different genres compared to a single model trained on a genre. |
Paraphrase Makes Perfect: Leveraging Expression Paraphrase to Improve Implicit Sentiment Learning (2025.coling-main)
Copied to clipboard
| Challenge: | Existing implicit sentiment learning methods focus on capturing implicit sentiment knowledge individually, without considering the potential connection between implicit and explicit sentiment. |
| Approach: | They propose an expression paraphrase strategy and a sentiment-consistent contrastive learning mechanism to learn the connections between implicit and explicit sentiment expressions and integrate them into the model. |
| Outcome: | The proposed method is effective on implicit sentiment analysis on public datasets. |
Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering (2026.acl-long)
Copied to clipboard
| Challenge: | Existing studies on neurons focus on emotion and rhetoric, neglecting their intrinsic connections. |
| Approach: | They propose a framework for fine-grained steering of emotion and rhetoric in large language models . they propose 'neuro-based' masking method that integrates multi-dimensional screening . |
| Outcome: | The proposed method achieves directed induction of non-target sentences and enhancement of emotion tasks via rhetoric neurons. |
Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces (N18-1)
Copied to clipboard
| Challenge: | Multi-task learning and semi-supervised learning are successful paradigms for learning in scenarios with limited labelled data. |
| Approach: | They propose to induce a joint embedding space between disparate label spaces and learning transfer functions between label embeddments to leverage unlabelled data and auxiliary, annotated datasets. |
| Outcome: | The proposed approach outperforms strong single and multi-task baselines and achieves state of the art on aspect-based and topic-based sentiment analysis. |
Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures (2021.naacl-main)
Copied to clipboard
| Challenge: | Prior work on identifying narratives related to sexual abuse disclosures did not consider this as an independent task. |
| Approach: | They propose to identify narratives related to sexual abuse disclosures as a joint modeling task that leverages their emotional attributes through multitask learning. |
| Outcome: | The proposed model leverages emotional attributes of textual conversations to identify narratives related to sexual abuse disclosures in homogeneous and heterogeneously settings. |
Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences. |
| Approach: | They propose to use a self-attention based encoder and a decoder with dot product attention mechanism to generate a viable response with a specified emotion. |
| Outcome: | The proposed model outperforms baselines on automatic evaluation measures such as F1 and BLEU scores, thus resulting in more fluent and adequate responses. |