CodeBERT pre-trains a bimodal model on code and text pairs plus unimodal data to achieve state-of-the-art results on natural language code search and code documentation generation.
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Reinforce-NAT and FS-decoder retrieve target sequential information for non-autoregressive translation, yielding higher BLEU than baseline NAT while preserving fast decoding and approaching autoregressive quality.
A multimodal Transformer ingests image features plus multiple external entity label sources and learns to control their appearance in fluent output captions.
citing papers explorer
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CodeBERT: A Pre-Trained Model for Programming and Natural Languages
CodeBERT pre-trains a bimodal model on code and text pairs plus unimodal data to achieve state-of-the-art results on natural language code search and code documentation generation.
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Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation
Reinforce-NAT and FS-decoder retrieve target sequential information for non-autoregressive translation, yielding higher BLEU than baseline NAT while preserving fast decoding and approaching autoregressive quality.
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Informative Image Captioning with External Sources of Information
A multimodal Transformer ingests image features plus multiple external entity label sources and learns to control their appearance in fluent output captions.