logo image
  • News
    • People Moves
    • Deal Wins
    • Demand Drivers
    • M&A and Funding
    • Financial Results
    • Technology
    • Academia
    • Industry News
    • Features
    • Machine Translation
    • — Divider —
    • Slator Pro
    • — Divider —
    • Press Releases
    • Sponsored Content
  • Data & Research
    • Research Reports & Pro Guides
    • Language Industry Investor Map
    • Real-Time Charts of Listed LSPs
    • Language Service Provider Index
  • Podcasts & Videos
  • Events
    • SlatorCon Remote May 2021
    • Email Marketing for Freelance Linguists
    • Preparing for the Critical Google Update Coming in May 2021
    • — Divider —
    • SlatorCon Coverage
    • Other Events
  • Directory
  • RFP Center
  • Jobs
MENU
  • News
    • People Moves
    • Deal Wins
    • Demand Drivers
    • M&A and Funding
    • Financial Results
    • Technology
    • Academia
    • Industry News
    • Features
    • Machine Translation
    • — Divider —
    • Slator Pro
    • — Divider —
    • Press Releases
    • Sponsored Content
  • Data & Research
    • Research Reports & Pro Guides
    • Language Industry Investor Map
    • Real-Time Charts of Listed LSPs
    • Language Service Provider Index
  • Podcasts & Videos
  • Events
    • SlatorCon Remote May 2021
    • Email Marketing for Freelance Linguists
    • Preparing for the Critical Google Update Coming in May 2021
    • — Divider —
    • SlatorCon Coverage
    • Other Events
  • Directory
  • RFP Center
  • Jobs

Register For Email Marketing for Freelance Linguists and Learn How To Win New Clients.

  • Slator Market Intelligence
  • Slator Advertising Services
  • Slator Advisory
  • Login
Search
Generic filters
Exact matches only
Advertisement
MIT Tackles the Ultimate Low-Resource Machine Translation Challenge

4 months ago

October 30, 2020

MIT Tackles the Ultimate Low-Resource Machine Translation Challenge

Academia ·

by Marion Marking

On October 30, 2020

4 months ago
Academia ·

by Marion Marking

On October 30, 2020

MIT Tackles the Ultimate Low-Resource Machine Translation Challenge

Big Tech’s focus on the translation of low-resource languages was recently highlighted when Facebook, on October 18, 2020, unveiled a model that would avoid using English as a pivot language between source and target languages. As reported by Slator, it was the “culmination of years of […] work in machine translation.”

Before that, there was Google’s research on massively multilingual neural machine translation (NMT) published back in July 2019, and more recent research on what the search giant calls “Complete Multilingual Neural Machine Translation.” As mentioned, the resulting NMT model that improved translation for languages with sparse training data was five years in the making.

Now from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) comes a model that can “automatically decipher a lost language without needing advanced knowledge of its relation to other languages.”

Advertisement

The CSAIL research team was led by MIT Professor Regina Barzilay, who has spent a couple of decades on language-related research, among other data science-driven topics. The team’s ultimate goal is for the system to be able to decipher lost languages that have eluded linguists for decades, using just a few thousand words,” wrote Adam Conner-Simons, CSAIL Communications Manager, on the lab’s blog.

SlatorCon Remote May 2021 | Super Early Bird $ 98

SlatorCon Remote May 2021 | Super Early Bird $ 98

A rich online conference which brings together our research and network of industry leaders.

Register Now

The study was, in part, supported by US spy agency IARPA. Over the years, IARPA has invested in finding low-resource language models that can be queried in English by holding conferences, offering grants, and running contests.

Ultimate Low-Resource Challenge for Humans and Machines

The new CSAIL study builds on a 2019 paper, where the authors, including Barzilay, propose a new approach to the automatic decipherment of lost languages. “Decipherment is an ultimate low-resource challenge for both humans and machines. The lack of parallel data and scarce quantities of ancient text complicate the adoption of neural methods that dominate modern machine translation,” the researchers wrote.

Slator 2021 Data-for-AI Market Report

Slator 2021 Data-for-AI Market Report

Data and Research, Slator reports
44-pages on how LSPs enter and scale in AI Data-as-a-service. Market overview, AI use cases, platforms, case studies, sales insights.
$380 BUY NOW

However, while in the 2019 paper the languages used were known to be related to early forms of Hebrew and Greek, in the new CSAIL study — which evaluated the model on Gothic, Ugaritic, and Iberian — the relationship between languages is inferred by algorithm and thus, applicable to more undeciphered scripts than prior work.

Using the algorithm, the team was able to confirm recent scholarship that suggests Iberian is not actually related to Basque as previously believed.

According to Conner-Simons, “The team hopes to expand their work beyond the act of connecting texts to related words in a known language — an approach referred to as ‘cognate-based decipherment.’ This paradigm assumes that such a known language exists, but the example of Iberian shows that this is not always the case. The team’s new approach would involve identifying semantic meaning of the words, even if they don’t know how to read them.”

Image: Phaistos Disk, an undecoded clay disk from the Minoan palace of Phaistos, Crete dating to the Minoan Bronze Age; exhibited in the Heraklion Archaeological Museum, Crete, Greece.

TAGS

Adam Conner-SimonsCSAILIARPAlow-resource languagesmachine translationMITNMTRegina Barzilay
SHARE
Marion Marking

By Marion Marking

Slator consultant and corporate communications professional who enjoys exploring Asian cities.

Advertisement

SUBSCRIBE TO THE SLATOR WEEKLY

Language Industry Intelligence
In Your Inbox. Every Friday

SUBSCRIBE

SlatorSweepSlatorPro
ResearchRFP CENTER

PUBLISH

PRESS RELEASEDIRECTORY LISTING
JOB ADEVENT LISTING

Bespoke advisory including speaking, briefings and M&A

SLATOR ADVISORY
Advertisement

Featured Reports

See all
Slator 2020 Language Industry M&A and Funding Report

Slator 2020 Language Industry M&A and Funding Report

by Slator

Slator 2021 Data-for-AI Market Report

Slator 2021 Data-for-AI Market Report

by Slator

Slator 2020 Medtech Translation and Localization Report

Slator 2020 Medtech Translation and Localization Report

by Slator

Pro Guide: Sales and Marketing for Language Service Providers

Pro Guide: Sales and Marketing for Language Service Providers

by Slator

Press Releases

See all
6CONNEX to Partner with Interprefy to Help Clients Host Large Scale Events in Any Language

6CONNEX to Partner with Interprefy to Help Clients Host Large Scale Events in Any Language

by Interprefy

BLEND Raises $10m to Fuel Global Growth with End-to-end Localization Services

BLEND Raises $10m to Fuel Global Growth with End-to-end Localization Services

by BLEND

Iconic Launches INTRA Translation Platform

Iconic Launches INTRA Translation Platform

by Iconic

Upcoming Events

See All
  1. Smartling - Global Ready Conference 2021

    Global Ready Conference

    by Smartling

    · April 14

    When you can't traverse the world, let the world come to you. Join our annual global event from home.

    More info FREE

Featured Companies

See all
Sunyu Transphere

Sunyu Transphere

Text United

Text United

Memsource

Memsource

Wordbank

Wordbank

Protranslating

Protranslating

Seprotec

Seprotec

Versacom

Versacom

Smartling

Smartling

XTM International

XTM International

Translators without Borders

Translators without Borders

STAR Group

STAR Group

memoQ Translation Technologies

memoQ Translation Technologies

Advertisement

Popular articles

Poland Rules on LSP Using Google Translate; Defines ‘Professional Translator’

Poland Rules on LSP Using Google Translate; Defines ‘Professional Translator’

by Marion Marking

The Slator 2021 Language Service Provider Index

The Slator 2021 Language Service Provider Index

by Slator

Behind the Scenes of the European Parliament’s Pivot to Remote Interpreting

Behind the Scenes of the European Parliament’s Pivot to Remote Interpreting

by Seyma Albarino

SlatorPod: The Weekly Language Industry Podcast

connect with us

footer logo

Slator makes business sense of the language services and technology market.

Our Company

  • Support
  • About us
  • Terms & Conditions
  • Privacy Policy

Subscribe to the Slator Weekly

Language Industry Intelligence
In Your Inbox. Every Friday

© 2021 Slator. All rights reserved.

Sign up to the Slator Weekly

Join over 13,500 subscribers and get the latest language industry intelligence every Friday

Your information will never be shared with third parties. No Spam.