ProSeqo: Projection Sequence Networks for On-Device Text Classification

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Authors: Zornitsa Kozareva,Sujith Ravi
Where published: IJCNLP 2019 11
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Abstract URL: https://www.aclweb.org/anthology/D19-1402/


We propose a novel on-device sequence model for text classification using recurrent projections. Our model ProSeqo uses dynamic recurrent projections without the need to store or look up any pre-trained embeddings. This results in fast and compact neural networks that can perform on-device inference for complex short and long text classification tasks. We conducted exhaustive evaluation on multiple text classification tasks. Results show that ProSeqo outperformed state-of-the-art neural and on-device approaches for short text classification tasks such as dialog act and intent prediction. To the best of our knowledge, ProSeqo is the first on-device long text classification neural model. It achieved comparable results to previous neural approaches for news article, answers and product categorization, while preserving small memory footprint and maintaining high accuracy.

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