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1
Developing Conversational Data and Detection of Conversational Humor in Telugu ...
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2
Detecting Gender Bias using Explainability ...
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3
Characterizing Test Anxiety on Social Media ...
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4
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
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5
Open Aspect Target Sentiment Classification with Natural Language Prompts ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.509/ Abstract: For many business applications, we often seek to analyze sentiments associated with any arbitrary aspects of commercial products, despite having a very limited amount of labels or even without any labels at all. However, existing aspect target sentiment classification (ATSC) models are not trainable if annotated datasets are not available. Even with labeled data, they fall short of reaching satisfactory performance. To address this, we propose simple approaches that better solve ATSC with natural language prompts, enabling the task under zero-shot cases and enhancing supervised settings, especially for few-shot cases. Under the few-shot setting for SemEval 2014 Task 4 laptop domain, our method of reformulating ATSC as an NLI task outperforms supervised SOTA approaches by up to 24.13 accuracy points and 33.14 macro F1 points. Moreover, we demonstrate that our prompts could handle implicitly stated aspects as well: our models reach ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Inference; Natural Language Processing; Sentiment Analysis
URL: https://dx.doi.org/10.48448/xy0p-qf26
https://underline.io/lecture/37815-open-aspect-target-sentiment-classification-with-natural-language-prompts
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6
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis ...
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7
Connecting Attributions and QA Model Behavior on Realistic Counterfactuals ...
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8
Solving Aspect Category Sentiment Analysis as a Text Generation Task ...
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9
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks ...
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10
Improving Multimodal fusion via Mutual Dependency Maximisation ...
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11
Does Commonsense help in detecting Sarcasm? ...
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12
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories ...
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13
How much coffee was consumed during EMNLP 2019? Fermi Problems: A New Reasoning Challenge for AI ...
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14
Towards Label-Agnostic Emotion Embeddings ...
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15
The Effect of Round-Trip Translation on Fairness in Sentiment Analysis ...
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16
Multimodal Phased Transformer for Sentiment Analysis ...
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17
MATE: Multi-view Attention for Table Transformer Efficiency ...
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18
Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph ...
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19
Classifying Dyads for Militarized Conflict Analysis ...
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20
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation Detection ...
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