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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 ...
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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 ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.319/ Abstract: Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion. Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence labeling task and a sentence relation classification task. However, despite the promising performance, such an approach obtains the argument pairs implicitly by the two decomposed tasks, lacking explicitly modeling of the argument-level interactions between argument pairs. In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage. In this manner, two passages can mutually guide each other in the process of APE. Furthermore, we propose an inter-sentence relation graph to effectively model the interrelations between two sentences and thus facilitates the ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Sentiment Analysis
URL: https://dx.doi.org/10.48448/kgf8-zx61
https://underline.io/lecture/37680-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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