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This paper introduces a novel binarized LSTM language model (BLLM) aimed at reducing memory consumption for natural language processing tasks, particularly on low-resource devices. By encoding words as binary vectors and binarizing LSTM parameters, the model achieves significant memory compression ratios of 11.3 and 31.6 with minimal performance loss. Experiments demonstrate that the binarized embeddings maintain effective performance in language modeling and automatic speech recognition tasks.

Author

shravanhegde123

Language

EN