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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.159 Abstract: Text generation has received a lot of attention in computational argumentation research as of recent. A particularly challenging task is the generation of {\em counter}-arguments. So far, approaches largely focus on rebutting a given conclusion, yet other ways to counter an argument exist. In this work, we go beyond previous research by exploring argument {\em undermining}, that is, countering an argument by attacking one of its premises. We hypothesize that identifying the argument's weak premises is key to effective countering. Accordingly, we propose an approach to assess the strength of a premise to then generate a counter-argument specifically targeting the weak ones. On the one hand, both manual and automatic evaluation proves the importance of identifying weak premises in counter-argument generation. On the other hand, when considering {\em correctness} and {\em content richness}, human annotators favored our approach over ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/26250-counter-argument-generation-by-attacking-weak-premises
https://dx.doi.org/10.48448/ybwz-c365
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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 ...
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