Pseudo-Label Training and Model Inertia in Neural Machine Translation

• URL: http://arxiv.org/abs/2305.11808v1
• Date: Fri, 19 May 2023 16:45:19 GMT
• Title: Pseudo-Label Training and Model Inertia in Neural Machine Translation
• Authors: Benjamin Hsu, Anna Currey, Xing Niu, Maria N\u{a}dejde and Georgiana Dinu
• Abstract summary: neural machine translation (NMT) models are sensitive to small input changes and can show significant variation across re-training or incremental model updates. This work studies a frequently used method in NMT, pseudo-label training (PLT), which is common to the related techniques of forwardtranslation or self-training. While the effect of quality is well-documented, we highlight a lesser-known effect:PL can enhance a model's stability to model updates and input perturbations.
• Score: 18.006833174265612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Like many other machine learning applications, neural machine translation (NMT) benefits from over-parameterized deep neural models. However, these models have been observed to be brittle: NMT model predictions are sensitive to small input changes and can show significant variation across re-training or incremental model updates. This work studies a frequently used method in NMT, pseudo-label training (PLT), which is common to the related techniques of forward-translation (or self-training) and sequence-level knowledge distillation. While the effect of PLT on quality is well-documented, we highlight a lesser-known effect: PLT can enhance a model's stability to model updates and input perturbations, a set of properties we call model inertia. We study inertia effects under different training settings and we identify distribution simplification as a mechanism behind the observed results.

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