用神经网络动态构建输入法搜索空间，提升预测准确率。
Neural Search Space in Gboard Decoder
- 用神经语言模型替代传统N-gram，动态生成搜索路径。
- 在多个地区测试中，错字率降低0.26%至1.19%。
- 适合想提升输入法准确率的开发者与研究者。
Gboard 输入法通过在上下文感知的搜索空间中寻找最佳匹配输入触点的路径来生成建议，该搜索空间由语言有限状态转换器（FST）支持。当前语言FST基于N-gram语言模型（LM），但受限于上下文长度，在设备模型大小约束下存在稀疏性问题。本文提出「神经搜索空间」，将N-gram LM替换为神经网络语言模型（NN-LM），并在解码过程中动态构建搜索空间。具体而言，通过在运行时将NN-LM对上下文的输出转换为语言FST，融入其长程上下文感知能力。这涉及语言FST结构重构、剪枝策略调优和数据结构优化。在线实验表明，质量显著提升，在不同地区错字率降低[0.26%, 1.19%]，延迟增加可接受。该工作为更直接地增强神经语言模型以改善输入法解码质量开辟了新路径。
原文摘要 · Abstract (English)
Gboard Decoder produces suggestions by looking for paths that best match input touch points on the context aware search space, which is backed by the language Finite State Transducers (FST). The language FST is currently an N-gram language model (LM). However, N-gram LMs, limited in context length, are known to have sparsity problem under device model size constraint. In this paper, we propose \textbf{Neural Search Space} which substitutes the N-gram LM with a Neural Network LM (NN-LM) and dynamically constructs the search space during decoding. Specifically, we integrate the long range context awareness of NN-LM into the search space by converting its outputs given context, into the language FST at runtime. This involves language FST structure redesign, pruning strategy tuning, and data structure optimizations. Online experiments demonstrate improved quality results, reducing Words Modified Ratio by [0.26\%, 1.19\%] on various locales with acceptable latency increases. This work opens new avenues for further improving keyboard decoding quality by enhancing neural LM more directly.
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