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Hits 81 – 100 of 1.029

81
Meta Distant Transfer Learning for Pre-trained Language Models ...
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82
How to Train BERT with an Academic Budget ...
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83
Improving Span Representation for Domain-adapted Coreference Resolution ...
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84
Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media ...
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85
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing ...
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86
Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy ...
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87
MRF-Chat: Improving Dialogue with Markov Random Fields ...
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88
Exploring Metaphoric Paraphrase Generation ...
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89
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization ...
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90
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech ...
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91
HypMix: Hyperbolic Interpolative Data Augmentation ...
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92
STaCK: Sentence Ordering with Temporal Commonsense Knowledge ...
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93
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning ...
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94
Weakly supervised discourse segmentation for multiparty oral conversations ...
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95
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution ...
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96
Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding ...
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97
Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.179/ Abstract: Although pre-training models have achieved great success in dialogue generation, their performance drops dramatically when the input contains an entity that does not appear in pre-training and fine-tuning datasets (unseen entity). To address this issue, existing methods leverage an external knowledge base to generate appropriate responses. In real-world scenario, the entity may not be included by the knowledge base or suffer from the precision of knowledge retrieval. To deal with this problem, instead of introducing knowledge base as the input, we force the model to learn a better semantic representation by predicting the information in the knowledge base, only based on the input context. Specifically, with the help of a knowledge base, we introduce two auxiliary training objectives: 1) Interpret Masked Word, which conjectures the meaning of the masked entity given the context; 2) Hypernym Generation, which predicts the hypernym of ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
URL: https://underline.io/lecture/37618-knowledge-enhanced-fine-tuning-for-better-handling-unseen-entities-in-dialogue-generation
https://dx.doi.org/10.48448/pq9s-y744
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98
STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media ...
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99
IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation ...
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100
SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis ...
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