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1910.13461
Cited By
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Annual Meeting of the Association for Computational Linguistics (ACL), 2019
29 October 2019
M. Lewis
Yinhan Liu
Naman Goyal
Marjan Ghazvininejad
Abdel-rahman Mohamed
Omer Levy
Veselin Stoyanov
Luke Zettlemoyer
AIMat
VLM
Re-assign community
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Papers citing
"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension"
50 / 5,155 papers shown
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KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding
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Jaewoong Yoon
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How to Evaluate a Summarizer: Study Design and Statistical Analysis for Manual Linguistic Quality Evaluation
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Anchit Gupta
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Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations
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El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing
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Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
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Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
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Practical Blind Membership Inference Attack via Differential Comparisons
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Prefix-Tuning: Optimizing Continuous Prompts for Generation
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Graphmax for Text Generation
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Intent Classification and Slot Filling for Privacy Policies
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Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences
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Neural Machine Translation: A Review of Methods, Resources, and Tools
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FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
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UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
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246
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