When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting

TL;DR

This paper introduces a constrained positional model with sparse attention for efficient word representation estimation, demonstrating improved speed, interpretability, and performance over previous models in language modeling tasks.

Contribution

The paper proposes a constrained sparse attention mechanism for positional models, enhancing speed and interpretability while maintaining or improving language modeling performance.

Findings

Constrained positional model trains twice as fast as the original.

Models contain interpretable grammatical information.

Outperforms other shallow models on language modeling.

Abstract

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.