14 citations · 20 across the 2 of their papers we have counts for
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cs.CL2018
Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension
Matthias Blohm, Glorianna Jagfeld, Ekta Sood +2
We propose a machine reading comprehension model based on the compare-aggregate framework with two-staged attention that achieves state-of-the-art results on the MovieQA question a…
cs.CL2017★ 6 cited
A General-Purpose Tagger with Convolutional Neural Networks
Xiang Yu, Agnieszka Faleńska, Ngoc Thang Vu
We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information. The CNN tagger is robust ac…
cs.CL2017★ 14 cited
Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages
Xiang Yu, Ngoc Thang Vu
We present a transition-based dependency parser that uses a convolutional neural network to compose word representations from characters. The character composition model shows grea…