Multi Hop refers to an architecture in which data is transmitted through multiple intermediate nodes (hops) between the source and destination. Instead of a direct point-to-point communication, where data is sent directly from the source to the destination, multi-hop communication involves relaying information through a series of intermediate nodes. The use of multi hop communication is driven by the need to extend communication range, improve network coverage, and create resilient and scalable network architectures in scenarios where direct point-to-point communication is impractical or inefficient.


Team Papelo at FEVEROUS: Multi-hop Evidence Pursuit

We develop a system for the FEVEROUS fact extraction and verification task that ranks an initial set of potential evidence and then pursues missing evidence in subsequent hops by trying to generate it, with a “next hop prediction module” whose output is matched against page elements in a predicted article. Seeking evidence with the next hop prediction module continues to improve FEVEROUS score for up to seven hops. Label classification is trained on possibly incomplete extracted evidence chains, utilizing hints that facilitate numerical comparison. The system achieves .281 FEVEROUS score and .658 label accuracy on the development set, and finishes in second place with .259 FEVEROUS score and .576 label accuracy on the test set.

Generating Followup Questions for Interpretable Multi hop Question Answering

We propose a framework for answering open domain multi hop questions in which partial information is read and used to generate followup questions, to finally be answered by a pretrained single hop answer extractor. This framework makes each hop interpretable, and makes the retrieval associated with later hops as flexible and specific as for the first hop. As a first instantiation of this framework, we train a pointer generator network to predict followup questions based on the question and partial information. This provides a novel application of a neural question generation network, which is applied to give weak ground truth single hop followup questions based on the final answers and their supporting facts. Learning to generate followup questions that select the relevant answer spans against downstream supporting facts, while avoiding distracting premises, poses an exciting semantic challenge for text generation. We present an evaluation using the two hop bridge questions of HotpotQA