Oracle Linguistic Graphs Complement a Pretrained Transformer Language Model: A Cross-formalism Comparison

• URL: http://arxiv.org/abs/2112.07874v1
• Date: Wed, 15 Dec 2021 04:29:02 GMT
• Title: Oracle Linguistic Graphs Complement a Pretrained Transformer Language Model: A Cross-formalism Comparison
• Authors: Jakob Prange, Nathan Schneider, Lingpeng Kong
• Abstract summary: We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. We find that, overall, semantic constituency structures are most useful to language modeling performance.
• Score: 13.31232311913236
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. With an ensemble setup consisting of a pretrained Transformer and ground-truth graphs from one of 7 different formalisms, we find that, overall, semantic constituency structures are most useful to language modeling performance -- outpacing syntactic constituency structures as well as syntactic and semantic dependency structures. Further, effects vary greatly depending on part-of-speech class. In sum, our findings point to promising tendencies in neuro-symbolic language modeling and invite future research quantifying the design choices made by different formalisms.

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