Improving Prediction Backward-Compatiblility in NLP Model Upgrade with Gated Fusion

Abstract

When upgrading neural models to a newer version, new errors that were not encountered in the legacy version can be introduced, known as regression 1 errors. This inconsistent behavior during model upgrade often outweighs the benefits of accuracy gain and hinders the adoption of new models. To mitigate regression errors from model upgrade, distillation and ensemble have proven to be viable solutions without significant compromise in performance. Despite the progress, these approaches attained an incremental reduction in regression which is still far from achieving backwardcompatible model upgrade. In this work, we propose a novel method, Gated Fusion, that promotes backward compatibility via learning to mix predictions between old and new models. Empirical results on two distinct model upgrade scenarios show that our method reduces the number of regression errors by 62% on average, outperforming the strongest baseline by an average of 25%. * Work done during author's internship at AWS AI Labs. 1 Within this work, regression denotes performance degradation in software systems, instead of the statistical technique for estimating relationships among variables.