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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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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.21/ Abstract: Multimodal sentiment analysis is a trending area of research, and the multimodal fusion is one of its most active topic. Acknowledging humans communicate through a variety of channels (i.e visual, acoustic, linguistic), multimodal systems aim at integrating different unimodal representations into a synthetic one. So far, a consequent effort has been made on developing complex architectures allowing the fusion of these modalities. However, such systems are mainly trained by minimising simple losses such as $L_1$ or cross-entropy. In this work, we investigate unexplored penalties and propose a set of new objectives that measure the dependency between modalities. We demonstrate that our new penalties lead to a consistent improvement (up to $4.3$ on accuracy) across a large variety of state-of-the-art models on two well-known sentiment analysis datasets: \texttt{CMU-MOSI} and \texttt{CMU-MOSEI}. Our method not only achieves a new SOTA ...
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Keyword:
Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis
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URL: https://dx.doi.org/10.48448/s46n-jz30
https://underline.io/lecture/37438-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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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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| 19 |
Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph ...
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