179 citations · 1.4k across the 46 of their papers we have counts for
8 papers · 1 filter
Multi-Perspective Context Matching for Machine Comprehension
Zhiguo Wang, Haitao Mi, Wael Hamza +1
Previous machine comprehension (MC) datasets are either too small to train end-to-end deep learning models, or not difficult enough to evaluate the ability of current MC techniques…
AMR-to-text generation as a Traveling Salesman Problem
Linfeng Song, Yue Zhang, Xiaochang Peng +2
The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR grap…
Supervised Attentions for Neural Machine Translation
Haitao Mi, Zhiguo Wang, Abe Ittycheriah
In this paper, we improve the attention or alignment accuracy of neural machine translation by utilizing the alignments of training sentence pairs. We simply compute the distance b…
Sense Embedding Learning for Word Sense Induction
Linfeng Song, Zhiguo Wang, Haitao Mi +1
Conventional word sense induction (WSI) methods usually represent each instance with discrete linguistic features or cooccurrence features, and train a model for each polysemous wo…
Vocabulary Manipulation for Neural Machine Translation
Haitao Mi, Zhiguo Wang, Abe Ittycheriah
In order to capture rich language phenomena, neural machine translation models have to use a large vocabulary size, which requires high computing time and large memory usage. In th…
Coverage Embedding Models for Neural Machine Translation
Haitao Mi, Baskaran Sankaran, Zhiguo Wang +1
In this paper, we enhance the attention-based neural machine translation (NMT) by adding explicit coverage embedding models to alleviate issues of repeating and dropping translatio…
Sentence Similarity Learning by Lexical Decomposition and Composition
Zhiguo Wang, Haitao Mi, Abraham Ittycheriah
Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and seman…
Semi-supervised Clustering for Short Text via Deep Representation Learning
Zhiguo Wang, Haitao Mi, Abraham Ittycheriah
In this work, we propose a semi-supervised method for short text clustering, where we represent texts as distributed vectors with neural networks, and use a small amount of labeled…