Global Autoregressive Models for Data-Efficient Sequence Learning

TL;DR

This paper introduces Global Autoregressive Models (GAMs), which combine autoregressive and log-linear components to improve data efficiency in sequence learning, especially under small-data conditions.

Contribution

The paper proposes GAMs that integrate global features with autoregressive models and a two-step training process involving unnormalized models and distillation for normalized inference.

Findings

Significant perplexity reduction with GAMs in language modeling

Effective use of global features to compensate for limited data

Two-step training improves inference speed and accuracy

Abstract

Standard autoregressive seq2seq models are easily trained by max-likelihood, but tend to show poor results under small-data conditions. We introduce a class of seq2seq models, GAMs (Global Autoregressive Models), which combine an autoregressive component with a log-linear component, allowing the use of global \textit{a priori} features to compensate for lack of data. We train these models in two steps. In the first step, we obtain an \emph{unnormalized} GAM that maximizes the likelihood of the data, but is improper for fast inference or evaluation. In the second step, we use this GAM to train (by distillation) a second autoregressive model that approximates the \emph{normalized} distribution associated with the GAM, and can be used for fast inference and evaluation. Our experiments focus on language modelling under synthetic conditions and show a strong perplexity reduction of using the second autoregressive model over the standard one.