A Deep Memory-based Architecture for Sequence-to-Sequence Learning

By Fandong Meng, Zhengdong Lu, Zhaopeng Tu, Hang Li, Qun Liu. 2015

20 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Minlie Huang
2. Biao Zhang
3. Hao Zhou
4. Xiaoyan Zhu
5. Zi-Yi Dou
6. Deyi Xiong
7. Shuming Shi
8. Jinsong Su

Citations in the corpus

• Exploiting Deep Representations for Neural Machine Translation (2018, methodology): “Concerning machine translation, Meng et al. (2016) and Zhou et al. (2016) have shown that deep networks with advanced connecting strategies outperform their shallow counterparts.”
• Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory (2017, methodology): “Inspired by these models, many other memory networks have been proposed for tasks such as machine translation (Meng et al., 2015), question answering (Miller et al.”
• Variational Neural Machine Translation (2016, methodology): “Due to these advantages over traditional SMT system, NMT has recently attracted growing interests from both deep learning and machine translation community (Kalchbrenner and Blunsom, 2013; Cho et al., 2014; Sutskever et al., 2014; Bahdanau et al., 2014; Luong et al., 2015a; Luong et al., 2015b; Shen”