Improving Answer Generation with Long-Document Transformers

In the world of natural language processing, transformer models have become the gold standard for many tasks. However, these models often struggle when dealing with long documents that require reasoning over multiple parts. To address this challenge, researchers propose a new approach called GEMFormer, which consists of two stages: collecting relevant information and combining it with local context to solve the task.
The first stage involves gathering relevant information from the entire document to create a memory that captures the most important facts. This memory is then used as input to the second stage, where attention-based token representations are combined with the memory to facilitate multi-hop reasoning. The result is a two-stage method that can effectively process long documents and perform complex reasoning tasks.
The proposed approach is evaluated on several benchmark datasets and shows significant improvements over existing methods. The authors also provide an in-depth analysis of the GEMFormer architecture, highlighting its strengths and limitations.
In summary, Longformer is a new transformer-based model that addresses the challenges of processing long documents by introducing a two-stage approach. The first stage collects relevant information from the entire document, while the second stage combines attention-based token representations with the memory to perform multi-hop reasoning. The proposed method shows promising results and has the potential to significantly improve the state-of-the-art in natural language processing tasks.