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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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Perceived and Intended Sarcasm Detection with Graph Attention Networks ...
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Abstract:
Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and interpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user's conversational neighborhood in an interaction graph, to contextualize the interpretation of the post. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with dense user history representations. Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our results indicate that the model contributes to interpreting the sarcastic intentions of an author more than to predicting the sarcasm perception by others. ...
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Keyword:
Deep Learning; Machine Learning; Natural Language Processing; Sentiment Analysis
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URL: https://underline.io/lecture/39399-perceived-and-intended-sarcasm-detection-with-graph-attention-networks https://dx.doi.org/10.48448/w8rk-hv22
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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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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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