CUNI NMT System for WAT 2017 Translation Tasks

WS 2017  ·  Tom Kocmi, Du{\v{s}}an Vari{\v{s}}, Ond{\v{r}}ej Bojar ·

The paper presents this year{'}s CUNI submissions to the WAT 2017 Translation Task focusing on the Japanese-English translation, namely Scientific papers subtask, Patents subtask and Newswire subtask. We compare two neural network architectures, the standard sequence-to-sequence with attention (Seq2Seq) and an architecture using convolutional sentence encoder (FBConv2Seq), both implemented in the NMT framework Neural Monkey that we currently participate in developing. We also compare various types of preprocessing of the source Japanese sentences and their impact on the overall results. Furthermore, we include the results of our experiments with out-of-domain data obtained by combining the corpora provided for each subtask.

PDF Abstract

Code
Add Remove Mark official

No code implementations yet. Submit your code now

Datasets

  Add Datasets introduced or used in this paper

Results from the Paper
Add Remove

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods
Add Remove

No methods listed for this paper. Add relevant methods here
Contact us on: hello@paperswithcode.com . Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Data policy Cookies policy