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Found 2 papers, 1 papers with code
Parameter-Efficient Neural Question Answering Models via Graph-Enriched Document Representations
In this paper, we suggest that large language models are not necessary for good performance by showing a na\"{i}ve implementation of a GCN performs comparably to SoTA models based on pretrained language models.
End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models
In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained language model.
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