ACL 2017 first day review

Machine Translation Session

A Convolutional Encoder Model for Neural Machine Translation

Jonas Gehring, Michael Auli, David Grangier and Yann Dauphin

  • The encoder is replaced by convolutional networks
  • Position embeddings are used
  • Two-stack architecture: there are two separate nets predicting the key and value for the attention
  • Interestingly, the author does not use two-stack architecture in their later research

Deep Neural Machine Translation with Linear Associative

Mingxuan Wang, Zhengdong Lu, Jie Zhou and Qun Liu

  • The residual connection is introduced inside the gate function, modified from GRU
  • It’s quite interesting the gain is very large by just replacing GRU with LAU

Alternative Objective Functions for Training MT Evaluation

Miloš Stanojević and Khalil Sima’an

  • A training-based evaluation method for machine translation
  • The evaluate itself is done by looking at the kendall’s tau agianst the human ranking data

General review of the conference

The accecpted papers tend to have a detailed comparison with other related methods, rather than just interesting. Beside the experiments, the structure of paper is usually clear and supports the core idea. This point is also reflected by classifying the sentiment (figure below). In the poster session, I have found the papers very diverse, there are quite a lot papers solving problems that I didn’t heard about.

Many people have different backgrounds, and some of them are not in the academia.People I have talked include entrepreneurs, recruiters and general managers. The nice thing about the hotel is the free coffee and food. Awesome experience.