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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.473 Abstract: Given a set of related publications, related work section generation aims to provide researchers with an overview of the specific research area by summarizing these works and introducing them in a logical order. Most of existing related work generation models follow the inflexible extractive style, which directly extract sentences from multiple original papers to form a related work discussion. Hence, in this paper, we propose a Relation-aware Related work Generator (RRG), which generates an abstractive related work from the given multiple scientific papers in the same research area. Concretely, we propose a relation-aware multi-document encoder that relates one document to another according to their content dependency in a relation graph. The relation graph and the document representation are interacted and polished iteratively, complementing each other in the training process. We also contribute two public datasets composed of related ...
URL: https://dx.doi.org/10.48448/xwxx-2227
https://underline.io/lecture/25915-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 ...
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