87 citations · 179 across the 13 of their papers we have counts for
22 papers
Augmenting Policy Learning with Routines Discovered from a Single Demonstration
Zelin Zhao, Chuang Gan, Jiajun Wu +2
Humans can abstract prior knowledge from very little data and use it to boost skill learning. In this paper, we propose routine-augmented policy learning (RAPL), which discovers ro…
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning
Xiaoxiao Guo, Mo Yu, Yupeng Gao +3
Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques. In contrast to previous text g…
Frustratingly Hard Evidence Retrieval for QA Over Books
Xiangyang Mou, Mo Yu, Bingsheng Yao +4
A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth. We for…
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering
Wenhan Xiong, Mo Yu, Xiaoxiao Guo +4
A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain…
Do Multi-hop Readers Dream of Reasoning Chains?
Haoyu Wang, Mo Yu, Xiaoxiao Guo +3
General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to deriv…
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering
Ameya Godbole, Dilip Kavarthapu, Rajarshi Das +8
Multi-hop question answering (QA) requires an information retrieval (IR) system that can find \emph{multiple} supporting evidence needed to answer the question, making the retrieva…