2.5k citations · 4.3k across the 50 of their papers we have counts for
4 papers · 2 filters
Stochastic Answer Networks for Machine Reading Comprehension
Xiaodong Liu, Yelong Shen, Kevin Duh +1
We propose a simple yet robust stochastic answer network (SAN) that simulates multi-step reasoning in machine reading comprehension. Compared to previous work such as ReasoNet whic…
An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks
Yelong Shen, Xiaodong Liu, Kevin Duh +1
Reading comprehension (RC) is a challenging task that requires synthesis of information across sentences and multiple turns of reasoning. Using a state-of-the-art RC model, we empi…
FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen +1
This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. First, it puts forward a novel concept of "histo…
Dynamic Fusion Networks for Machine Reading Comprehension
Yichong Xu, Jingjing Liu, Jianfeng Gao +2
This paper presents a novel neural model - Dynamic Fusion Network (DFN), for machine reading comprehension (MRC). DFNs differ from most state-of-the-art models in their use of a dy…