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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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Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection ...
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Comparative Opinion Quintuple Extraction from Product Reviews ...
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PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction ...
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Natural language processing as a tool to identify the Reddit particularities of cancer survivors around the time of diagnosis and remission: A pilot study ...
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A Large-Scale Dataset for Empathetic Response Generation ...
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YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews ...
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Sample Selection Guided by Domain and Task for Cross-Domain Targeted Sentiment Analysis ...
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Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training ...
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Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis ...
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Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge ...
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Unimodal and Crossmodal Refinement Network for Multimodal Sequence Fusion ...
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Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.24/ Abstract: Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification. However, existing studies aimed to improve the generalization ability by augmenting the training data with synonymous examples or adding random noises to word embeddings, which cannot address the spurious association problem. In this work, we propose an end-toend reinforcement learning framework, which jointly performs counterfactual data generation and dual sentiment classification. Our approach has three characteristics: 1) the generator automatically generates massive and diverse antonymous sentences; 2) the discriminator contains a original-side sentiment predictor and an antonymous-side sentiment predictor, which jointly evaluate the quality of the generated sample and help the generator iteratively generate ...
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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://dx.doi.org/10.48448/5v66-xt12 https://underline.io/lecture/38097-reinforced-counterfactual-data-augmentation-for-dual-sentiment-classification
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Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model ...
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