Papers with vocabulary pruning

2 papers
TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models (2022.acl-demo)

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Challenge: Large pre-trained language models have been used for many NLP tasks but computational resources are limited.
Approach: They propose an open-source model pruning toolkit for pre-trained language models . they propose a self-supervised pruning method that can be applied without labeled data.
Outcome: The proposed pruning method reduces model size without retraining the model and speeds up inference speed on the common CPU and GPU devices.
Transferable Neural Projection Representations (N19-1)

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Challenge: Neural word embeddings require lookup and a large memory footprint making it hard to deploy on-device.
Approach: They propose a skip-gram based architecture coupled with Locality-Sensitive Hashing projections to learn efficient dynamically computable representations.
Outcome: The proposed model performs better than previous models on multiple NLP tasks.

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