Neural and Statistical Methods for Leveraging Meta-information in Machine Translation

08/10/2017
by   Shahram Khadivi, et al.
0

In this paper, we discuss different methods which use meta information and richer context that may accompany source language input to improve machine translation quality. We focus on category information of input text as meta information, but the proposed methods can be extended to all textual and non-textual meta information that might be available for the input text or automatically predicted using the text content. The main novelty of this work is to use state-of-the-art neural network methods to tackle this problem within a statistical machine translation (SMT) framework. We observe translation quality improvements up to 3

READ FULL TEXT
research
08/20/2017

Neural Machine Translation with Extended Context

We investigate the use of extended context in attention-based neural mac...
research
12/18/2019

A Survey on Document-level Machine Translation: Methods and Evaluation

Machine translation (MT) is an important task in natural language proces...
research
09/29/2015

Neural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts

The quality of machine translation is rapidly evolving. Today one can fi...
research
07/05/2016

Target-Side Context for Discriminative Models in Statistical Machine Translation

Discriminative translation models utilizing source context have been sho...
research
03/25/2017

Simplifying the Bible and Wikipedia Using Statistical Machine Translation

I started this work with the hope of generating a text synthesizer (like...
research
12/16/2016

Neural Networks Classifier for Data Selection in Statistical Machine Translation

We address the data selection problem in statistical machine translation...
research
09/06/2019

Self Learning from Large Scale Code Corpus to Infer Structure of Method Invocations

Automatically generating code from a textual description of method invoc...

Please sign up or login with your details

Forgot password? Click here to reset