Papers with Natural Languages

2 papers
OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages (2022.acl-long)

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Challenge: a new study examines the performance of pretraining for sign language recognition in low-resource settings.
Approach: They propose using pose extracted through pretrained models as the standard modality of data to reduce training time and enable efficient inference.
Outcome: The proposed model reduces training time and allows efficient inference in sign languages.
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation (2021.emnlp-main)

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Challenge: Pre-trained models for Natural Languages (NL) like BERT and GPT have been shown to transfer well to Programming Languages.
Approach: They propose a unified pre-trained encoder-decoder Transformer model that leverages the code semantics conveyed from the developer-assigned identifiers.
Outcome: The proposed model outperforms existing models on understanding and generation tasks and can capture semantic information from code.

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