  Transformers have become the gold standard for many natural language
processing tasks and, in particular, for multi-hop question answering (MHQA).
This task includes processing a long document and reasoning over the multiple
parts of it. The landscape of MHQA approaches can be classified into two
primary categories. The first group focuses on extracting supporting evidence,
thereby constraining the QA model's context to predicted facts. Conversely, the
second group relies on the attention mechanism of the long input encoding model
to facilitate multi-hop reasoning. However, attention-based token
representations lack explicit global contextual information to connect
reasoning steps. To address these issues, we propose GEMFormer, a two-stage
method that first collects relevant information over the entire document to the
memory and then combines it with local context to solve the task. Our
experimental results show that fine-tuning a pre-trained model with
memory-augmented input, including the most certain global elements, improves
the model's performance on three MHQA datasets compared to the baseline. We
also found that the global explicit memory contains information from supporting
facts required for the correct answer.
