We develop a unified system to answer directly from text open-domain
questions that may require a varying number of retrieval steps. We employ a
single multi-task transformer model to perform all the necessary subtasks --
retrieving supporting facts, reranking them, and predicting the answer from all
retrieved documents -- in an iterative fashion. We avoid crucial assumptions of
previous work that do not transfer well to real-world settings, including
exploiting knowledge of the fixed number of retrieval steps required to answer
each question or using structured metadata like knowledge bases or web links
that have limited availability. Instead, we design a system that can answer
open-domain questions on any text collection without prior knowledge of
reasoning complexity. To emulate this setting, we construct a new benchmark,
called BeerQA, by combining existing one- and two-step datasets with a new
collection of 530 questions that require three Wikipedia pages to answer,
unifying Wikipedia corpora versions in the process. We show that our model
demonstrates competitive performance on both existing benchmarks and this new
benchmark. We make the new benchmark available at this https URL

通过使用单个多任务 transformer 模型，我们设计了一个统一的系统，可以迭代地检索支持事实，重新排序它们，并从所有检索的文档中预测答案，从而回答各种需要不同数量的检索步骤的开放领域问题。我们构建了一个新的基准测试 BeerQA，并证明我们的模型在现有基准测试和这个新基准测试上都表现出有竞争力的表现。