The MITLL-AFRL IWSLT 2016 Systems

Abstract

This report summarizes the MITLL-AFRL MT and ASR systems and the experiments run during the 2016 IWSLT evaluation campaign. Building on lessons learned from previous years results, we refine our ASR systems and examine the explosion of neural machine translation systems and techniques developed in the past year. We experiment with a variety of phrase-based, hierarchical and neural-network approaches in machine translation and utilize system combination to create a composite system with the best characteristics of all MT approaches.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 08, 2016
Accession Number
AD1030409

Entities

People

  • Brian Ore
  • Brian Thompson
  • Elizabeth Salesky
  • Eric C Hansen
  • Grant Erdmann
  • Jeremy Gwinnup
  • Jonathon Taylor
  • Katherine Young
  • Michaeel Kazi
  • Michael Hutt
  • Timothy Anderson

Organizations

  • MIT Lincoln Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Automated Speech Recognition
  • Coding
  • Computational Science
  • Decoding
  • Foreign Languages
  • Hidden Markov Models
  • Language
  • Language Translation
  • Machine Translation
  • Markov Models
  • Models
  • Natural Language Processing
  • Neural Networks
  • Probability
  • Vocabulary

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Translation
  • AI & ML - Neural Networks