Torch-Struct: Deep Structured Prediction Library

• URL: http://arxiv.org/abs/2002.00876v1
• Date: Mon, 3 Feb 2020 16:43:02 GMT
• Title: Torch-Struct: Deep Structured Prediction Library
• Authors: Alexander M. Rush
• Abstract summary: We introduce Torch-Struct, a library for structured prediction. Torch-Struct includes a broad collection of probabilistic structures accessed through a simple and flexible distribution-based API.
• Score: 138.5262350501951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 https://github.com/harvardnlp/pytorch-struct.

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