Haitao Mi

Sentence Similarity Learning by Lexical Decomposition and Composition

2016 · arXiv (Cornell University)

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 semantic meanings about the sentences. In this work, …

Temporal Attention Model for Neural Machine Translation

2016 · arXiv (Cornell University)

Attention-based Neural Machine Translation (NMT) models suffer from attention deficiency issues as has been observed in recent research. We propose a novel mechanism to address some of these limitations and improve the NMT attention. Specifically, …

Supervised Attentions for Neural Machine Translation

2016

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 between the machine attentions and the "true" alignments, …

Coverage Embedding Models for Neural Machine Translation

2016

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 translations in NMT. For each source word, our model starts with …