NTT's Machine Translation Systems for WMT19 Robustness Task

07/09/2019
by   Soichiro Murakami, et al.
0

This paper describes NTT's submission to the WMT19 robustness task. This task mainly focuses on translating noisy text (e.g., posts on Twitter), which presents different difficulties from typical translation tasks such as news. Our submission combined techniques including utilization of a synthetic corpus, domain adaptation, and a placeholder mechanism, which significantly improved over the previous baseline. Experimental results revealed the placeholder mechanism, which temporarily replaces the non-standard tokens including emojis and emoticons with special placeholder tokens during translation, improves translation accuracy even with noisy texts.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/21/2019

CUNI System for the WMT19 Robustness Task

We present our submission to the WMT19 Robustness Task. Our baseline sys...
research
10/31/2019

Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness

We share a French-English parallel corpus of Foursquare restaurant revie...
research
07/15/2019

Naver Labs Europe's Systems for the WMT19 Machine Translation Robustness Task

This paper describes the systems that we submitted to the WMT19 Machine ...
research
10/28/2020

The Volctrans Machine Translation System for WMT20

This paper describes our VolcTrans system on WMT20 shared news translati...
research
06/19/2019

Robust Machine Translation with Domain Sensitive Pseudo-Sources: Baidu-OSU WMT19 MT Robustness Shared Task System Report

This paper describes the machine translation system developed jointly by...
research
06/10/2021

Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021

This paper describes our systems that were submitted to the restricted t...
research
12/03/2020

CUT: Controllable Unsupervised Text Simplification

In this paper, we focus on the challenge of learning controllable text s...

Please sign up or login with your details

Forgot password? Click here to reset