Linguistic Profiling of a Neural Language Model

TL;DR

This paper examines how BERT's linguistic knowledge, learned before and after fine-tuning, influences its classification performance across various linguistic features and tasks.

Contribution

It provides a comprehensive analysis of BERT's linguistic encoding capabilities and their impact on prediction accuracy during fine-tuning.

Findings

BERT encodes a wide range of linguistic features.

Fine-tuning reduces BERT's linguistic information.

More linguistic information correlates with better predictions.

Abstract

In this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems. We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that BERT is able to encode a wide range of linguistic characteristics, but it tends to lose this information when trained on specific downstream tasks. We also find that BERT's capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information of a sentence, the higher will be its capacity of predicting the expected label assigned to that sentence.