  Multi-hop Question Answering (QA) is a challenging task because it requires
precise reasoning with entity relations at every step towards the answer. The
relations can be represented in terms of labels in knowledge graph (e.g.,
\textit{spouse}) or text in text corpus (e.g., \textit{they have been married
for 26 years}). Existing models usually infer the answer by predicting the
sequential relation path or aggregating the hidden graph features. The former
is hard to optimize, and the latter lacks interpretability. In this paper, we
propose TransferNet, an effective and transparent model for multi-hop QA, which
supports both label and text relations in a unified framework. TransferNet
jumps across entities at multiple steps. At each step, it attends to different
parts of the question, computes activated scores for relations, and then
transfer the previous entity scores along activated relations in a
differentiable way. We carry out extensive experiments on three datasets and
demonstrate that TransferNet surpasses the state-of-the-art models by a large
margin. In particular, on MetaQA, it achieves 100\% accuracy in 2-hop and 3-hop
questions. By qualitative analysis, we show that TransferNet has transparent
and interpretable intermediate results.
