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Hits 81 – 100 of 1.423

81
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions ...
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82
Evidence-based Factual Error Correction ...
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83
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images ...
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84
A DQN-based Approach to Finding Precise Evidences for Fact Verification ...
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85
Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event ...
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86
A Span-based Dynamic Local Attention Model for Sequential Sentence Classification ...
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87
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection ...
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88
2A: Sentiment Analysis, Stylistic Analysis, and Argument Mining #1 ...
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89
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation ...
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90
How effective is BERT without word ordering? Implications for language understanding and data privacy ...
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91
GEM: Natural Language Generation, Evaluation, and Metrics - Part 4 ...
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92
The statistical advantage of automatic NLG metrics at the system level ...
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93
Counter-Argument Generation by Attacking Weak Premises ...
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94
Supporting Cognitive and Emotional Empathic Writing of Students ...
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95
What's in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus ...
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96
Are Pretrained Convolutions Better than Pretrained Transformers? ...
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97
Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? ...
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98
Beyond Offline Mapping: Learning Cross-lingual Word Embeddings through Context Anchoring ...
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99
Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation ...
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100
Hate Speech Detection Based on Sentiment Knowledge Sharing ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.556 Abstract: The wanton spread of hate speech on the internet brings great harm to society and families. It is urgent to establish and improve automatic detection and active avoidance mechanisms for hate speech. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. In other words, getting more affective features from other affective resources will significantly affect the performance of hate speech detection. In this paper, we propose a hate speech detection framework based on sentiment knowledge sharing. While extracting the affective features of the target sentence itself, we make better use of the sentiment features from external resources, and finally fuse features from different feature extraction units to detect hate speech. Experimental results on two public datasets demonstrate the effectiveness of our model. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25794-hate-speech-detection-based-on-sentiment-knowledge-sharing
https://dx.doi.org/10.48448/xa78-5h98
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