1.5k citations · 2.6k across the 5 of their papers we have counts for
6 papers
Dilated Recurrent Neural Networks
Shiyu Chang, Yang Zhang, Wei Han +7
Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and explod…
R: Reinforced Reader-Ranker for Open-Domain Question Answering
Shuohang Wang, Mo Yu, Xiaoxiao Guo +7
In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results i…
Improved Neural Relation Detection for Knowledge Base Question Answering
Mo Yu, Wenpeng Yin, Kazi Saidul Hasan +3
Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural netwo…
A Structured Self-attentive Sentence Embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos +4
This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the em…
Comparative Study of CNN and RNN for Natural Language Processing
Wenpeng Yin, Katharina Kann, Mo Yu +1
Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main t…
Embedding Lexical Features via Low-Rank Tensors
Mo Yu, Mark Dredze, Raman Arora +1
Modern NLP models rely heavily on engineered features, which often combine word and contextual information into complex lexical features. Such combination results in large numbers…