Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning

• URL: http://arxiv.org/abs/2010.02357v1
• Date: Mon, 5 Oct 2020 21:56:00 GMT
• Title: Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning
• Authors: Tsvetomila Mihaylova, Vlad Niculae, Andr\'e F. T. Martins
• Abstract summary: Latent structure models are a powerful tool for modeling language data. One challenge with end-to-end training of these models is the argmax operation, which has null gradient. We explore latent structure learning through the angle of pulling back the downstream learning objective.
• Score: 20.506232306308977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Latent structure models are a powerful tool for modeling language data: they can mitigate the error propagation and annotation bottleneck in pipeline systems, while simultaneously uncovering linguistic insights about the data. One challenge with end-to-end training of these models is the argmax operation, which has null gradient. In this paper, we focus on surrogate gradients, a popular strategy to deal with this problem. We explore latent structure learning through the angle of pulling back the downstream learning objective. In this paradigm, we discover a principled motivation for both the straight-through estimator (STE) as well as the recently-proposed SPIGOT - a variant of STE for structured models. Our perspective leads to new algorithms in the same family. We empirically compare the known and the novel pulled-back estimators against the popular alternatives, yielding new insight for practitioners and revealing intriguing failure cases.

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