787 citations · 2.3k across the 19 of their papers we have counts for
4 papers · 1 filter
RTFM: Generalising to Novel Environment Dynamics via Reading
Victor Zhong, Tim Rocktäschel, Edward Grefenstette
Obtaining policies that can generalise to new environments in reinforcement learning is challenging. In this work, we demonstrate that language understanding via a reading policy l…
Multi-hop Reading Comprehension through Question Decomposition and Rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer +1
Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system for multi-hop RC that decomposes a compositional question int…
E3: Entailment-driven Extracting and Editing for Conversational Machine Reading
Victor Zhong, Luke Zettlemoyer
Conversational machine reading systems help users answer high-level questions (e.g. determine if they qualify for particular government benefits) when they do not know the exact ru…
Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering
Victor Zhong, Caiming Xiong, Nitish Shirish Keskar +1
End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear c…