A Classification Approach to Word Prediction

TL;DR

This paper introduces a novel word prediction method that leverages expressive context representations with external knowledge and an attention mechanism, demonstrating significant improvements in prediction accuracy.

Contribution

It presents a new approach combining external knowledge-based context representations and an attention mechanism for improved word prediction.

Findings

Significant accuracy improvements in word prediction tasks

Effective use of external knowledge for context representation

Demonstrated scalability in large-scale experiments

Abstract

The eventual goal of a language model is to accurately predict the value of a missing word given its context. We present an approach to word prediction that is based on learning a representation for each word as a function of words and linguistics predicates in its context. This approach raises a few new questions that we address. First, in order to learn good word representations it is necessary to use an expressive representation of the context. We present a way that uses external knowledge to generate expressive context representations, along with a learning method capable of handling the large number of features generated this way that can, potentially, contribute to each prediction. Second, since the number of words ``competing'' for each prediction is large, there is a need to ``focus the attention'' on a smaller subset of these. We exhibit the contribution of a ``focus of attention'' mechanism to the performance of the word predictor. Finally, we describe a large scale experimental study in which the approach presented is shown to yield significant improvements in word prediction tasks.