How does the input segmentation of pretrained language models (PLMs) affect their generalization capabilities ? We present the first study investigating this question, taking BERT as the example PLM and focusing on the semantic representations of derivationally complex words . 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 derivational segmentation consistently outperforms BERT 's WordPiece segmentationby a large margin . Our results suggest that the generalization capabilities of PLMs could be further improved if a morphologically-informed vocabulary of input tokens were used.
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.
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generalization capabilities
interpretations of complex words
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derivationally complex words
English derivatives
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derivational segmentation consistently outperforms BERT 's WordPiece segmentationby a large margin
DelBERT (Derivation leveraging BERT), a model with derivational input segmentation, substantially outperforms BERT with WordPiece segmentation