DeepAI
Log In Sign Up

Six Challenges for Neural Machine Translation

06/12/2017
by   Philipp Koehn, et al.
0

We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.

READ FULL TEXT

page 3

page 5

04/08/2021

Extended Parallel Corpus for Amharic-English Machine Translation

This paper describes the acquisition, preprocessing, segmentation, and a...
01/11/2017

A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions

We aim to shed light on the strengths and weaknesses of the newly introd...
09/10/2018

Towards one-shot learning for rare-word translation with external experts

Neural machine translation (NMT) has significantly improved the quality ...
05/31/2018

On the Impact of Various Types of Noise on Neural Machine Translation

We examine how various types of noise in the parallel training data impa...
10/03/2017

Improving Lexical Choice in Neural Machine Translation

We explore two solutions to the problem of mistranslating rare words in ...
09/03/2014

Overcoming the Curse of Sentence Length for Neural Machine Translation using Automatic Segmentation

The authors of (Cho et al., 2014a) have shown that the recently introduc...
09/26/2016

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Neural Machine Translation (NMT) is an end-to-end learning approach for ...

Code Repositories

nlp_tasks

Natural Language Processing Tasks and References


view repo