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Predicting emotional links between genre, plot, and reader response ...
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Developing Conversational Data and Detection of Conversational Humor in Telugu ...
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Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
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Open Aspect Target Sentiment Classification with Natural Language Prompts ...
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SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis ...
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Connecting Attributions and QA Model Behavior on Realistic Counterfactuals ...
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End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ...
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Solving Aspect Category Sentiment Analysis as a Text Generation Task ...
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CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks ...
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Improving Multimodal fusion via Mutual Dependency Maximisation ...
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Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories ...
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How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI ...
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Towards Label-Agnostic Emotion Embeddings ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.728/ Abstract: Research in emotion analysis is scattered across different label formats (e.g., polarity types, basic emotion categories, and affective dimensions), linguistic levels (word vs. sentence vs. discourse), and, of course, (few well-resourced but much more under- resourced) natural languages and text genres (e.g., product reviews, tweets, news). The resulting heterogeneity makes data and software developed under these conflicting constraints hard to compare and challenging to integrate. To resolve this unsatisfactory state of affairs we here propose a training scheme that learns a shared latent representation of emotion independent from different label formats, natural languages, and even disparate model architectures. Experiments on a wide range of datasets indicate that this approach yields the desired interoperability without penalizing prediction quality. Code and data are archived under DOI 10.5281/zenodo.5466068. ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis
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URL: https://underline.io/lecture/37839-towards-label-agnostic-emotion-embeddings https://dx.doi.org/10.48448/hn4a-jx95
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The Effect of Round-Trip Translation on Fairness in Sentiment Analysis ...
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MATE: Multi-view Attention for Table Transformer Efficiency ...
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Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph ...
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