GiBERT: Introducing Linguistic Knowledge into BERT through a Lightweight Gated Injection Method

• URL: http://arxiv.org/abs/2010.12532v1
• Date: Fri, 23 Oct 2020 17:00:26 GMT
• Title: GiBERT: Introducing Linguistic Knowledge into BERT through a Lightweight Gated Injection Method
• Authors: Nicole Peinelt, Marek Rei and Maria Liakata
• Abstract summary: We propose a novel method to explicitly inject linguistic knowledge in the form of word embeddings into a pre-trained BERT. Our performance improvements on multiple semantic similarity datasets when injecting dependency-based and counter-fitted embeddings indicate that such information is beneficial and currently missing from the original model.
• Score: 29.352569563032056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large pre-trained language models such as BERT have been the driving force behind recent improvements across many NLP tasks. However, BERT is only trained to predict missing words - either behind masks or in the next sentence - and has no knowledge of lexical, syntactic or semantic information beyond what it picks up through unsupervised pre-training. We propose a novel method to explicitly inject linguistic knowledge in the form of word embeddings into any layer of a pre-trained BERT. Our performance improvements on multiple semantic similarity datasets when injecting dependency-based and counter-fitted embeddings indicate that such information is beneficial and currently missing from the original model. Our qualitative analysis shows that counter-fitted embedding injection particularly helps with cases involving synonym pairs.

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