PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text
We consider open domain question answering (QA) where answers are drawn from either a corpus, a knowledge base (KB) or a combination of both of these. We focus on a setting in which a corpus is supplemented with a large but incomplete KB, and on questions that require non trivial (e.g. "multi hop" reasoning. We describe PullNet, an integrated framework for (1) learning what to retrieve and (2) reasoning with this heterogeneous information to find the best answer. PullNet uses an iterative process to construct a question specific subgraph that contains information relevant to the question. In each iteration, a graph convolutional network (graph CNN) is used to identify subgraph nodes that should be expanded using retrieval (or "pull" operations on the corpus and/or KB. After the subgraph is complete, another graph CNN is used to extract the answer from the subgraph. This retrieve and reason process allows us to answer multi hop questions using large KBs and corpora. PullNet is weakly supervised, requiring question answer pairs but not gold inference paths. Experimentally PullNet improves over the prior state of the art, and in the setting where a corpus is used with incomplete KB these improvements are often dramatic. PullNet is also often superior to prior systems in a KB only setting or a text only setting.
EMNLP