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Hits 61 – 80 of 1.168

61
Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech ...
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62
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning ...
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63
LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification ...
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64
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions ...
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65
Evidence-based Factual Error Correction ...
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66
SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images ...
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67
A DQN-based Approach to Finding Precise Evidences for Fact Verification ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.83 Abstract: Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV. Despite being important, precise evidences are rarely studied by existing methods for FV. It is challenging to find precise evidences due to a large search space with lots of local optimums. Inspired by the strong exploration ability of the deep Q-learning network (DQN), we propose a DQN-based approach to retrieval of precise evidences. In addition, to tackle the label bias on Q-values computed by DQN, we design a post-processing strategy which seeks best thresholds for determining the true labels of computed evidences. Experimental results confirm the effectiveness of DQN in computing precise evidences and demonstrate improvements in achieving ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/znnc-pt10
https://underline.io/lecture/25965-a-dqn-based-approach-to-finding-precise-evidences-for-fact-verification
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68
Embedding Time Differences in Context-sensitive Neural Networks for Learning Time to Event ...
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69
A Span-based Dynamic Local Attention Model for Sequential Sentence Classification ...
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70
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection ...
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71
2A: Sentiment Analysis, Stylistic Analysis, and Argument Mining #1 ...
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72
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation ...
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73
How effective is BERT without word ordering? Implications for language understanding and data privacy ...
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74
GEM: Natural Language Generation, Evaluation, and Metrics - Part 4 ...
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75
The statistical advantage of automatic NLG metrics at the system level ...
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76
Counter-Argument Generation by Attacking Weak Premises ...
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77
Supporting Cognitive and Emotional Empathic Writing of Students ...
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78
What's in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus ...
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79
Are Pretrained Convolutions Better than Pretrained Transformers? ...
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80
Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards? ...
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