Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words

TL;DR

This study investigates how input segmentation affects BERT's understanding of complex words, showing that derivational morphology-aware segmentation improves semantic interpretation and generalization.

Contribution

The paper introduces DelBERT, a derivational morphology-informed segmentation method that enhances BERT's semantic understanding of complex words compared to standard WordPiece segmentation.

Findings

DelBERT outperforms BERT on semantic probing tasks.

Morphologically-informed segmentation improves generalization to new words.

PLMs can be viewed as serial dual-route models for complex word interpretation.

Abstract

How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words? We present the first study investigating this question, taking BERT as the example PLM and focusing on its semantic representations of English derivatives. We show that PLMs can be interpreted as serial dual-route models, i.e., the meanings of complex words are either stored or else need to be computed from the subwords, which implies that maximally meaningful input tokens should allow for the best generalization on new words. This hypothesis is confirmed by a series of semantic probing tasks on which DelBERT (Derivation leveraging BERT), a model with derivational input segmentation, substantially outperforms BERT with WordPiece segmentation. Our results suggest that the generalization capabilities of PLMs could be further improved if a morphologically-informed vocabulary of input tokens were used.