This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016. In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.

1. ‘Past, Present, and Future’
Machine Translation & Natural Language
Processing for Patent Information
Dr. John Tinsley
CEO, Iconic Translation Machines Ltd.
EPOPIC. Madrid. 10th November 2016

2. BSc in Computational Linguistics
PhD in Machine Translation
Language Technology consultant
Founder of Iconic Translation Machines
Why listen to me?
Machine Translation is what I do!
The world’s first and only patent specific machine translation platform

3.  The use of computers to translate from one language into another
 The use of computers to automate some, or all, of the translation
process
 An approach to Machine Translation, where translations for an input are
estimated based on previous seen translation examples and associated
(inferred) probabilities.
 e.g. IPTranslator, Google Translate
 Rule-based (or transfer-based): based on linguistic rules
• e.g. Systran; Altavista’s Babelfish
 Example-based: based on translation examples and inferred linguistic
patterns
Machine Translation: The Basics
Machine Translation = automatic translation
Statistical Machine Translation (SMT)
Other approaches
SMT is now by far the predominant approach*

4. A corpus (pl. corpora) is a collection
of texts, in electronic format, in a
single language
 document(s)
 book(s)
Bilingual Corpora
a bilingual corpus
Note source language = original language or language we’re translating from
target language = language we’re translating into
A bilingual corpus is a collection of
corresponding texts, in multiple
languages
 a document & its translation
 a book in multiple languages
 European Parliament proceedings

5. Aligned Bilingual Corpora
A document-aligned bilingual corpus corresponds on a document level
For translation, we required sentence-aligned bilingual corpora
 The sentence on line 1 in the source language text corresponds
to (i.e. is a translation of) the sentence on line 1 in the target
language text etc.
 Often referred to as parallel aligned corpora
Sentence aligned bilingual parallel corpora
are essential for statistical machine translation

6. Learning from Previous Translations
Suppose we already know
(from a sentence-aligned bilingual
corpus) that:
 “dog” is translated as “perro”
 “I have a cat” is translated as
“Tengo un gato”
We can theoretically translate:
 “I have a dog”  “Tengo un perro”
 Even though we have never seen “I
have a dog” before
Statistical machine translation induces information about unseen input, based on
previously known translations:
 Primarily co-occurrence statistics
 Takes contextual information into account

7. Statistical Machine Translation
 Example of a small sentence-aligned
bilingual corpus for English-French

8. Statistical Machine Translation
 We take some new sentence to translate

9. Statistical Machine Translation
 From the corpus we can infer possible target (French)
translations for various source (English) words
 We can then select the most probable translations
based on simple frequencies (co-occurrence statistics)

10. Statistical Machine Translation
Given a previously unseen input sentence, and our collated statistics,
we can estimate translation

11. Advanced MT
All modern approaches are based on building translations for complete
sentences by putting together smaller pieces of translation
Previous example is very simplistic
 In reality SMT systems calculate much more complex statistical models
over millions of sentence pairs for a pair of languages
 Upwards of 2M sentence pairs on average for large-scale systems
 Word-to-word translation probabilities
 Phrase-to-phrase translation probabilities
 Word order probabilities
 Linguistic information (are the words nouns, verbs?)
 Fluency of the final output
Previous example is very simplistic
Other statistics calculated include

12. Data is Key
For SMT data is key
 Information (word/phrase correspondences and associated statistics) is only based
on what we have seen before in the data
Important that data used to train SMT systems is:
 Of sufficient size
 avoid sparseness/skewed statistics
 Representative and relevant
 contains the right type of language
 High-quality
 absence of misspellings,
incorrect alignments etc.
 Proofed by human
translators
training data

13. Why is MT Difficult?
A word or a phrase can have more than one meaning (ambiguity – lexical or
structural)
 e.g. “bank”, “dive”, “I saw the man with the telescope”
People use language creatively
 New words are cropping up all the time
Linguistic differences between languages
 e.g. structure of Irish sentences vs. structure of English sentences:
 “Tá (Is) ocras (hunger) orm (on me)” <-> “I am hungry”
There can be more than one way to express the same meaning.
 “New York”, “The Big Apple”, “NYC”

14. Why is MT Difficult?
 Israeli officials are responsible for airport security.
 Israel is in charge of the security at this airport.
 The security work for this airport is the responsibility of the Israel government.
 Israeli side was in charge of the security of this airport.
 Israel is responsible for the airport’s security.
 Israel is responsible for safety work at this airport.
 Israel presides over the security of the airport.
 Israel took charge of the airport security.
 The safety of this airport is taken charge of by Israel.
 This airport’s security is the responsibility of the Israeli security officials.

15. No single solution for all languages
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

16. No single solution for all languages
English - German
English - Chinese
种水果的农民
The farmer who grows fruit
[Lit: “grow fruit (particle) farmer”]

17. Not all languages are created equal
French German Turkish Finnish
Spanish Chinese Korean Hungarian
Portuguese Japanese Thai Basque

18. The Challenge of Patents
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

19. The Challenge of Patents
Very long sentences as standard
Grammatically incomplete using
nominal and telegraphic style (!)
Passive forms are frequent
Frequent use of subordinate clauses,
participles, implicit constructs
Inconsistent and incorrect spelling
High use of neologisms
Instances of synonymy and polysemy
Spurious use of punctuation
Authoring guide
for “to be
translated” text
Patents break
almost all of the
rules!

20. Judge the quality of an MT system by comparing its output against a
human-produced “reference” translation
 Pros: Quick, cheap, consistent
 Cons: Inflexible, cannot be used on ‘new’ input
 Pros: Reliable, flexible, multi-faceted (fluency, error analyses,
benchmarking)
 Cons: Slow, expensive, subjective
 Fluency vs. Adequacy
Evaluating Machine Translation Quality
Automatic Evaluation
Human Evaluation
Task-Based Evaluation

21. Evaluating Machine Translation Quality
Task Based Evaluation
 Standalone evaluation of MT systems is necessary to get a sense of the
overall quality of a system
 To determine the ultimate usability of an MT system, intrinsic task-based
evaluation is required
 Why? Fluency vs. Adequacy
Fluency how fluent and grammatically correct the translation
output is
Adequacy how accurately the translation conveys the meaning of the
source
Output 1 The big blue house
Output 2 The big house red
Source La gran casa roja
Task-Based Evaluation

22. Practical uses of Machine Translation
Understand its limitations and you’ll understand
its capabilities!
No
 Translate a patent for filing
 Translate literature for
publication
 Translate marketing
materials
 Anything mission critical
without review
Yes
 Productivity tool for
professional translation
 Understand foreign patents
 Localisation processes and
“controlled’ content
 High volume, e.g. eDiscovery

23. Use cases in practice
Product descriptions
to open new markets
MT for post-editing
productivity across
industries
Developer, and user
for web content
Tens of thousands of
people using online
tools daily

24. Neural Networks
 Using artificial intelligence and deep learning to develop a
completely new way of doing machine translation!
Quality Estimation
 Functionality through which machine translation can “self-
assess” the quality of the translations it produces.
Online Adaptive Translation
 Machine translations that can automatically learn and improve
based on feedback, particularly from revisions.
Use-case specific MT
 Just like patent MT, but for countless other areas.
Current Hot Topics

25. About Iconic
We are a Machine Translation and Natural
Language Processing software and
services provider, delivering expert
solutions with Subject Matter Expertise

26. Iconic Ensemble Architecture…

27. …enhanced with Neural MT

30. Speed, Cost, and Quality
What is the difference between machine translation vs. manual translation when
translating a 10 page patent document from Chinese into English?
Machine Translation is not
designed to replace
professional translation but
there are many cases
where costly and time-
consuming manual
translation is simply not
necessary.

31. - Data confidentiality
- File formats
- Potential for customisation,
enhancements, and
improvement for specific
domains

32. More than just translation
DATA PROCESSING
E.G. OPTICAL CHARACTER
RECOGNITION, DIGITISATION
DATABASE BUILDING
E.G. COMBINING THE ABOVE, WITH
TRANSLATION, FOR EXPORT
DATA UNDERSTANDING
E.G. SUMMARISATION, CONCEPT &
KEY TERM IDENTIFICATION
INFORMATION EXTRACTION
E.G. CITATION ANALYSIS, CROSS-
LINGUAL SEARCH

33. Record Extraction
Extraction algorithms work on cleaned
OCR output, using patterns, keywords,
and formatting information.

34. Citation Analysis
Assessment of record and reference patterns Application for record extraction
Tracking variations across years
Application for bibliographic data fielding

35. Reference extraction + fielding

36. .com
Visit
and use the promo code epo2016 to get 20
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37. Thank You!
john@iptranslator.com
@IconicTrans