Baskaran Sankaran

Zero-Resource Translation with Multi-Lingual Neural Machine Translation

2016 · arXiv (Cornell University)

In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way, mulitlingual neural machine translate that enables zero-resource machine translation. When used together with novel many-to-one translation strategies, we empirically show that …

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, …

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 …