“Data & Linguistics” Delivering Machine Translation with

Subject Matter Expertise

John TinsleyDirector / Co-Founder

Localization World. 31st Oct 2014, Vancouver

Machine Translation with Subject Matter Expertise

From Data Engineering toLinguistic Engineering

“Ensemble” MT architecture

The world’s first and only patent specific MT system that’s ready to go

Data EngineeringWhat is Linguistic Engineering?

Pre-processing Post-processing

Input Output

Training Data

Patents: an MT nightmare

L is an organic group selected from -CH2-(OCH2CH2)n-, -CO-NR'-, with R'=H or C1-C4 alkyl group; n=0-8; Y=F, CF3 …

maximum stress of 1.2 to 3.5 N/mm<2> and a maximum elongation of 700 to 1,300% at 0[deg.] C.

Long Sentences

Technical constructions

Largest single document: 249,322 words

Longest Sentence: 1,417 words

“Most of these things are not like the other”

Many languages aren’t a dream either

(And teaches the teacher her students language the Arabic)

Spanish – Italian English – Spanish Arabic – English

Data EngineeringWhat is Linguistic Engineering?

Pre-processing Post-processing

Input Output

Training Data

Data Engineering + Linguistic EngineeringAn “ensemble” architecture

Chinese pre-ordering rules

StatisticalPost-editing

Input

Output

Training Data

Spanish med-deviceentity recognizer Multi-output

Combination

Korean pharmatokenizer

Patent inputclassifier

Client TM/terminology (optional)

Japanese scriptnormalisation

GermanCompounding rules

Moses

RBMT

Moses

Moses

Easier said than done

“A very particular set of skills”

MT Knowledge

(from a scientific perspective)

Domain Knowledge

(the nature of the content)

Linguistic Knowledge

(the characteristics of the language)

MT Knowledge

Implementation

•  Computer science!•  Programming•  Data structures•  Algorithms

Science

•  Machine learning•  Probability theory•  Bayesian statistics•  Markov Models

Domain Knowledge

What’s important?

•  Chemical names•  References to figures•  Claim cross-references

Where do we learn?

•  Commercial partners•  LSPs & Translators•  Research

Consistent across langs?

•  Japanese abstract order•  Numbering / bullets•  Document layout

Document types?

•  Patents•  Applications, reports

•  Pharmaceutical•  IFUs, labels

Iconic Translation Machines

Linguistic Knowledge

Number agreement: the house / the houses vs. la maison / les maisons

Gender agreement: the house / the cheese vs. la maison / le frommage

English - Spanish

English - French

Linguistic Knowledge

English - German

English - Chinese

种水果的农民

The farmer who grows fruit[Lit: “grow fruit (particle) farmer”]

If you don’t understand it, you can’t translate it

MT with Subject Matter Expertise

“Allopurinol-induced serious cutaneous adverse reactions (SCAR), including Steven Johnson’s syndrome

(SJS) and toxic epidermal necrolysis (TEN), are associated with a genetic marker, the HLA-B*5801

allele.”

“IPTranslator is perfect for someone who needs to search [patents] across multiple languages and with is useful in the case of both patentability and infringement searches.”

– Aalt van de Kuilen, Global Head of Patent Information, Abbott

Machine Translation for Patents

What is the value for users?

Specialist solutions deliver more useable outcomes for the user

Post-editing

For information purposes

Multilingual search

Increased productivity

Extract more meaning

Retrieve more relevant results

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=

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De-risking the machine translation proposition

What is the value for users?

+ Data + Time + €€€ = ???

+ No data needed + Systems are ready to go + No upfront cost= Evaluate immediately

New PrerequisitesTypical Prerequisites

Customisation. Refinement.

» Incorporation of user feedback» Incremental training with post-edits» Tuning for specific input types

Case Studies 1.  What this approach means straight up in terms of quality…

2.  Productivity gains from using these systems…

3.  As a foundation for client customization…

Case 1: Quality

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Iconic

Google

Systran

Portuguese to English

Case 1: Quality

2.83

4 3.863.56

1

1.5

2

2.5

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3.5

4

4.5

5

Evaluator 1 Evaluator 2 Evaluator 3 Average

German to English TranslationGerman to English

Case 2: Productivity

Iconic had a domain-specific MT solution for that industry

Machine Translation technology for the legal industry

Business Need

Case 2: Productivity

Delivered immediately and initial results were positive

Translation samples required for initial evaluation

Process (1)

Case 2: Productivity

“The complexities and unforeseen but inevitable surprises of MT integration in large scale production processes were handled both competently and efficiently.”

Integrate Iconic with GlobalSight for productivity pilot

Process (2)

Case 2: Productivity

>20% productivity increase for translator post-editing Iconic output

“Measurable productivity gains delivered from the outset”

Performance

Case 2: Productivity

•  Ongoing improvement through feedback from translators•  Ongoing improvement through the incorporation of post-edits

•  More than 5 million words translated to date for Asian languages•  Periodic roll-out of new languages over time

Looking forward

Case 3: Customization

-  Modify our patent machine translation engines for “Written Opinions” on patents

-  0.25% new data, 2 new ensemble processes

21 20

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Iconic Google

+ Modification

Baseline

Chinese to English

Case 3: Customization

Productivitythreshold

Essentially out of domain – not viable for post-editing

Case 3: Customization

Productivitythreshold

After customization – 25% gain in productivity

All content is not created equal

We cannot afford to be dogmatic when it comes to MT

Know your subject matter!

Domain specific MT is about more than just data

Take home messages…

+ Linguistics!

Thank You! john@iptranslator.com

@IconicTrans