The literature on structured prediction for NLP describes a rich collection
of distributions and algorithms over sequences, segmentations, alignments, and
trees; however, these algorithms are difficult to utilize in deep learning
frameworks. We introduce Torch-Struct, a library for structured prediction
designed to take advantage of and integrate with vectorized,
auto-differentiation based frameworks. Torch-Struct includes a broad collection
of probabilistic structures accessed through a simple and flexible
distribution-based API that connects to any deep learning model. The library
utilizes batched, vectorized operations and exploits auto-differentiation to
produce readable, fast, and testable code. Internally, we also include a number
of general-purpose optimizations to provide cross-algorithm efficiency.
Experiments show significant performance gains over fast baselines and
case-studies demonstrate the benefits of the library. Torch-Struct is available
at this https URL

Torch-Struct 是一个用于结构化预测的库工具，使用基于向量化和自动微分的框架，并提供简单灵活的基于分布式 API 的概率结构来连接任何深度学习模型，并且经过内部的优化使其快速可读，在基准测试中表现良好。