  It is challenging for current one-step retrieve-and-read question answering
(QA) systems to answer questions like "Which novel by the author of 'Armada'
will be adapted as a feature film by Steven Spielberg?" because the question
seldom contains retrievable clues about the missing entity (here, the author).
Answering such a question requires multi-hop reasoning where one must gather
information about the missing entity (or facts) to proceed with further
reasoning. We present GoldEn (Gold Entity) Retriever, which iterates between
reading context and retrieving more supporting documents to answer open-domain
multi-hop questions. Instead of using opaque and computationally expensive
neural retrieval models, GoldEn Retriever generates natural language search
queries given the question and available context, and leverages off-the-shelf
information retrieval systems to query for missing entities. This allows GoldEn
Retriever to scale up efficiently for open-domain multi-hop reasoning while
maintaining interpretability. We evaluate GoldEn Retriever on the recently
proposed open-domain multi-hop QA dataset, HotpotQA, and demonstrate that it
outperforms the best previously published model despite not using pretrained
language models such as BERT.
