Learning to Navigate Wikipedia by Taking Random Walks

Manzil Zaheer, Kenneth Marino, Will Sussman Grathwohl, John Schultz, Wendy Shang, Sheila Babayan, Arun Ahuja, Ishita Dasgupta, Christine Kaeser-Chen, Rob Fergus

Keywords: web navigation, wikipedia, retrieval, fact verification, question answering

TL;DR: We present an efficient technique for learning to navigate web knowledge sources like Wikipedia by pretraining on random walks. This navigating agent can be used for precise evidence gathering on downstream tasks like QA and fact verification.

Abstract: A fundamental ability of an intelligent web-based agent is seeking out and acquiring new information. Internet search engines reliably find the correct vicinity but the top results may be a few links away from the desired target. A complementary approach is navigation via hyperlinks, employing a policy that comprehends local content and selects a link that moves it closer to the target. In this paper, we show that behavioral cloning of randomly sampled trajectories is sufficient to learn an effective link selection policy. We demonstrate the approach on a graph version of Wikipedia with 38M nodes and 387M edges. The model is able to efficiently navigate between nodes 5 and 20 steps apart 96% and 92% of the time, respectively. We then use the resulting embeddings and policy in downstream fact verification and question answering tasks where, in combination with basic TF-IDF search and ranking methods, they are competitive results to the state-of-the-art methods.

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/learning-to-navigate-wikipedia-by-taking/code)