Papers by Shumin Zhai

2 papers
Neural Search Space in Gboard Decoder (2024.emnlp-industry)

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Challenge: Gboard decoder uses context, a lexicon and language models to provide a user-friendly keyboard.
Approach: They propose a Neural Search Space which replaces an N-gram LM with a neural network LM and dynamically constructs the search space during decoding.
Outcome: The proposed system improves the quality of the decoded keyboards on various locales with acceptable latency increases.
Proofread: Fixes All Errors with One Tap (2024.acl-demos)

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Challenge: Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56% good ratio.
Approach: They propose a two-stage tuning approach to acquire the dedicated Large Language Model for the feature, followed by a reinforcement learning approach for targeted refinement.
Outcome: The proposed model achieves 85.56% good quality on Rewrite and proofread tasks on human-labeled golden sets.

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