  In 2018, Mikolov et al. introduced the positional language model, which has
characteristics of attention-based neural machine translation models and which
achieved state-of-the-art performance on the intrinsic word analogy task.
However, the positional model is not practically fast and it has never been
evaluated on qualitative criteria or extrinsic tasks. We propose a constrained
positional model, which adapts the sparse attention mechanism from neural
machine translation to improve the speed of the positional model. We evaluate
the positional and constrained positional models on three novel qualitative
criteria and on language modeling. We show that the positional and constrained
positional models contain interpretable information about the grammatical
properties of words and outperform other shallow models on language modeling.
We also show that our constrained model outperforms the positional model on
language modeling and trains twice as fast.
