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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.506 Abstract: Recent research on cross-lingual word embeddings has been dominated by unsupervised mapping approaches that align monolingual embeddings. Such methods critically rely on those embeddings having a similar structure, but it was recently shown that the separate training in different languages causes departures from this assumption. In this paper, we propose an alternative approach that does not have this limitation, while requiring a weak seed dictionary (e.g., a list of identical words) as the only form of supervision. Rather than aligning two fixed embedding spaces, our method works by fixing the target language embeddings, and learning a new set of embeddings for the source language that are aligned with them. To that end, we use an extension of skip-gram that leverages translated context words as anchor points, and incorporates self-learning and iterative restarts to reduce the dependency on the initial dictionary. Our approach outperforms ...
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/ykva-6168
https://underline.io/lecture/25725-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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