We introduce a framework for automatic differentiation with weighted
finite-state transducers (WFSTs) allowing them to be used dynamically at
training time. Through the separation of graphs from operations on graphs, this
framework enables the exploration of new structured loss functions which in
turn eases the encoding of prior knowledge into learning algorithms. We show
how the framework can combine pruning and back-off in transition models with
various sequence-level loss functions. We also show how to learn over the
latent decomposition of phrases into word pieces. Finally, to demonstrate that
WFSTs can be used in the interior of a deep neural network, we propose a
convolutional WFST layer which maps lower-level representations to higher-level
representations and can be used as a drop-in replacement for a traditional
convolution. We validate these algorithms with experiments in handwriting
recognition and speech recognition.

该论文提出了一种使用加权有限状态转录器（WFSTs）进行自动微分的框架，使它们可以在训练期间动态使用，该框架可以探索新的结构化损失函数并结合各种序列级损失函数使用剪枝和回退等方法，在字短语的潜在分解学习方面具有很大的优势，同时提出了一种卷积 WFST 层，可用作传统卷积层的替代，用于手写识别和语音识别的实验中表现良好。