  As the computational footprint of modern NLP systems grows, it becomes
increasingly important to arrive at more efficient models. We show that by
employing graph convolutional document representation, we can arrive at a
question answering system that performs comparably to, and in some cases
exceeds the SOTA solutions, while using less than 5\% of their resources in
terms of trainable parameters. As it currently stands, a major issue in
applying GCNs to NLP is document representation. In this paper, we show that a
GCN enriched document representation greatly improves the results seen in
HotPotQA, even when using a trivial topology. Our model (gQA), performs
admirably when compared to the current SOTA, and requires little to no
preprocessing. In Shao et al. 2020, the authors suggest that graph networks are
not necessary for good performance in multi-hop QA. 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.

Computation and Language