用神经网络学习状态结构，自动构建高效字符串转换器。
Neural Induction of Finite-State Transducers
- 从RNN隐状态几何中推导出无权有限状态转换器结构
- 在词形变化等任务上最高比传统方法准确率提升87%
- 适合需要高效推理的自然语言处理场景
有限状态转换器（FST）是字符串重写任务的有效模型，常用于高性能应用，但手动构造难度大。本文提出一种新方法，基于循环神经网络学习到的隐藏状态几何，自动生成无权有限状态转换器。在真实数据集上的实验表明，所构建的FST在词形变位、音素转音标和历史文本归一化任务中表现优异，对多个数据集具有高准确性和鲁棒性，其在保留测试集上的准确率最高比经典转换器学习算法高出87%。
原文摘要 · Abstract (English)
Finite-State Transducers (FSTs) are effective models for string-to-string rewriting tasks, often providing the efficiency necessary for high-performance applications, but constructing transducers by hand is difficult. In this work, we propose a novel method for automatically constructing unweighted FSTs following the hidden state geometry learned by a recurrent neural network. We evaluate our methods on real-world datasets for morphological inflection, grapheme-to-phoneme prediction, and historical normalization, showing that the constructed FSTs are highly accurate and robust for many datasets, substantially outperforming classical transducer learning algorithms by up to 87% accuracy on held-out test sets.
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