Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension
Table 7: Evidence Assembler comparison study: Reranker (described in the appendix) rescores the documents selected by the Document Explorer.
['[EMPTY]', 'full', 'follows + multiple', 'follows + single']
In order to justify our choice of building an Assembler, we build a 2-module system without the Evidence-Assembler stage by applying the Answer Proposer to only the top-1 reasoning chain in the tree. We also present two voting heuristics that selects the final answer by taking the average/maximum prediction probability from the Answer Proposer on all document chains. Furthermore, we compare our Evidence Assembler with an alternative model that, instead of assembling information from all reasoning chains, reranks all chains and their proposed answers to select the top-1 answer prediction. This demonstrates the importance of the Assembler in enabling information aggregation over multiple reasoning chains. The results further show that our Assembler is better than the reranking alternative.