Language Modelling via Learning to Rank

TL;DR

This paper proposes a novel approach to language modeling by framing it as a ranking task and using rank-based knowledge distillation, which improves perplexity and can be achieved without pre-trained models.

Contribution

It introduces a ranking-based framework for language modeling and demonstrates that simple N-gram teachers can be as effective as complex pre-trained models.

Findings

Rank-based KD improves perplexity over KL-based KD.

N-gram teachers are competitive with complex models.

GPT-2 is the most effective teacher among tested models.

Abstract

We consider language modelling (LM) as a multi-label structured prediction task by re-framing training from solely predicting a single ground-truth word to ranking a set of words which could continue a given context. To avoid annotating top-$k$ ranks, we generate them using pre-trained LMs: GPT-2, BERT, and Born-Again models. This leads to a rank-based form of knowledge distillation (KD). We also develop a method using $N$-grams to create a non-probabilistic teacher which generates the ranks without the need of a pre-trained LM. We confirm the hypotheses that we can treat LMing as a ranking task and that we can do so without the use of a pre-trained LM. We show that rank-based KD generally improves perplexity (PPL), often with statistical significance, when compared to Kullback-Leibler-based KD. Surprisingly, given the simplicity of the method, $N$-grams act as competitive teachers and achieve similar performance as using either BERT or a Born-Again model teachers. GPT-2 always acts as the best teacher, though, and using it and a Transformer-XL student on Wiki-02, rank-based KD reduces a cross-entropy baseline from 65.27 to 55.94 and against a KL-based KD of 56.70.