WebNav: A New Large-Scale Task for Natural Language based Sequential Decision Making

By Rodrigo Nogueira, Kyunghyun Cho. 2016

10 citations recorded by the source corpus.

Subfields: Natural Language Processing, Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Jianfeng Gao
2. Xiaodong He
3. Mohit Bansal
4. Weizhu Chen
5. Sebastian Riedel
6. Li Deng
7. Wen-tau Yih
8. Xiaodong Liu

In Reinforcement Learning

1. Sergey Levine
2. Pieter Abbeel
3. Lihong Li
4. Yuxi Li
5. Aviv Tamar
6. Shimon Whiteson
7. Chelsea Finn
8. Ofir Nachum

Citations in the corpus

• Value Iteration Networks (2016, methodology): ““WebNav” [20] is a recently proposed goal-driven web navigation benchmark.”
• Constructing Datasets for Multi-hop Reading Comprehension Across Documents (2017, background): “Learning Search Expansion Other research addresses expanding the document set available to a QA system, either in the form of web navigation (Nogueira and Cho, 2016), or via query reformulation techniques, which often use neural reinforcement learning (Narasimhan et al., 2016; Nogueira and Cho,…”
• ReasoNet: Learning to Stop Reading in Machine Comprehension (2016, methodology): “Unlike previous approaches using a xed number of hops or iterations, motivated by [15, 16], we propose a termination module in the inference.”
• Deep Reinforcement Learning with a Natural Language Action Space (2015, background): “More recently, Nogueira and Cho (2016) have also proposed a goal-driven web navigation task for language based sequential decision making study.”