Learning with Noise-Contrastive Estimation: Easing training by learning to scale

Matthieu Labeau (1) , Alexandre Allauzen (2)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

Noise-Contrastive Estimation (NCE) is a learning criterion that is regularly used to train neural language models in place of Maximum Likelihood Estimation, since it avoids the computational bottleneck caused by the output softmax. In this paper, we analyse and explain some of the weaknesses of this objective function, linked to the mechanism of self-normalization, by closely monitoring comparative experiments. We then explore several remedies and modifications to propose tractable and efficient NCE training strategies. In particular, we propose to make the scaling factor a trainable parameter of the model, and to use the noise distribution to initialize the output bias. These solutions, yet simple, yield stable and competitive performances in either small and large scale language modelling tasks.

Domains: Computation and Language [cs.CL]; Statistics [stat]

Matthieu Labeau, Alexandre Allauzen. Learning with Noise-Contrastive Estimation: Easing training by learning to scale. 27th International Conference on Computational Linguistics (COLING 2018), Aug 2018, Santa Fe, NM, United States. pp.3090-3101. ⟨hal-02912385⟩