In the framework of AAIT 18, where we discussed the level of awareness of T&I as a profession, I presented the idea that translators are mediators between clear communication and technology. 'Artifical Intelligence' is perhaps inadvertently omnipresent in our lives, from "intelligent' vacuum cleaners to Tolstoi write-alikes. And then there is the push for clear communication heralded by the Plain Language movement. Plain Language seems to suggest language that is machine translatable. However, I advocate for Plain Language 2.0: clear communication with style. Yes, machines can do a great deal but infusing communication with relevant flair and without sacrificing clarity is still a human endeavor, that's where sophisticated professionals come in.

1. Plain Language
+
Artificial Intelligence
=
Sophisticated Professional?
ROMINA MARAZZATO SPARANO

2. Agenda
1. Brief Overview of
AI
2. Brief Overview
of Plain Language
3. The Translator in the Middle:
Professionalization

3. Some problems lend themselves more easily to A.I. solutions than others. So
hate speech is one of the hardest, because determining if something is hate
speech is very linguistically nuanced, right?
"It's -- you need to understand, you know, what is a slur and what --
whether something is hateful not just in English, but the majority of people
on Facebook use it in languages that are different (...)
"Hate speech -- I am optimistic that, over a 5- to 10-year period, we will
have A.I. tools that can get into some of the nuances -- the linguistic
nuances of different types of content to be more accurate in flagging things
for our systems.
"But, today, we're just not there on that. So a lot of this is still reactive.
People flag it to us. We have people look at it. We have policies to try to
make it as not subjective as possible. But, until we get it more automated,
there is a higher error rate than I'm happy with.
Zuckerberg Statement Excerpt

4. Plain Language Australia

5. “Artificial
Intelligence”
> Based on Neural
Networks

6. > Escher-like Space

7. Artificial
Intelligence
 Algorithms & Logic
 Flow Charts
 Formulas

8. Sets of
instructions

9. “Artificial Intelligence”
 NLP: Natural Language
Processing
> Parts of Speech
> Parsing Trees
> Lego-like processing… if
not too fancy

10. “Artificial Intelligence”
 Machine Learning
> Statistical processing
after tagging
 Deep Learning
> Semantic webs with
tagging capabilities
Knowledge Graph:
billions of nodes!

11. “Artificial Intelligence”
 Multidimensional space “translated” into networks & vectors
 Learn through experience “translated” into data processing
 Perception “translated” into incorporation of visual/auditory
processing (input & output)

12. Neural Networks
Analogy Task by machine
Man is to Woman like
 King is to….
 Einstein is to scientist like
 Messi is to…
 Mozart is to…
 Picasso is to…
Japan is to sushi like
 Germany is to
 France is to
 Queen
 Midfielder
 Violinist
 Painter
 Bratwurst
 Tapas

13. Writing Task
True Love: variation of
Tolstoy’s “Anna Karenina”
written in the style of a
Japanese author

14.  Locating
 Understanding
 Acting upon
… relevant
information
Plain Language is about…

15. the idiomatic
and grammatical
use of language
that most effectively
presents ideas
to the reader

16.  Citizen
Information
 Service
Access
 Consumer
Needs
Government Bureaucracy
Legal Jargon
Corporate Filler
Citizen Information
Service Access
Consumer Needs
Literacy & Diversity
Science Communication
& Policymaking
 Research & Development
 Plain Language 1.0
 Plain Language 2.0
 Gobbledygook

17. Plain Language
Strategies
PL Text ::
APT3
Text Content
Purpose
Text Design
Text Flow

18. Consider your reader
 questions, interests, needs
Beware the Curse of Knowledge!
 contextualize appropriately

19.  Address your readers needs
from their point of view

Children, Disabilities,
2nd Language

General Public

Specialists

20. Purpose
Lead with your topic
 scaffolding for learning & action
Plan a clear destination
 set goals & expectations

21. Text Flow
Organize your information
 headlines, sections, lists …
Structure your ideas
 hierarchy, sequence, Q&A …

22. Text Content
Use grammar sense!
 length, word order, parsing
Build your discourse
 cohesion, coherence

23. Name Piling
Buried cable technician
Gamma-aminobutiric acid

24. Text DesignText Design
Catch your readers’ eye
 Formatting, space, visual elements
Use data visualization
 Infographics, pattern libraries

25. Patient
instructions

26. Recap
 Definition
 Strategies: APT3
 Audience: … Needs & Context
 Purpose: … Topic & Relevance
 Text Flow: … Sections & Structure
 Text Content: … Parsing & Coherence
 Text Design … Formatting & Data Visualization
Purpose
Text Flow
Text Content
Text Design

27. JUDGE: The charge here is theft of frozen chickens.
Are you the defendant?
DEFENDANT: No, sir, I'm the guy who stole the chickens.*
•preponderance of the evidence =
“slow and careful pondering of the evidence”
•speculate = ? burden of proof = ?
*Disorder in the Coutroom, Sevilla.

28. Subject Matter Writers
Readers
LegaleseLearning
Disabilities
I
Immigration &
2nd language
AcademeseLimited
Schooling
Bureaucratese

29. Written Language & the Brain
The brain constructs the message
differently for reading and listening

30. Locating information | Low level Inference | High-level Inference
verbatim | | & Interpretation
TOMATO SOUP
$1.15
Weight: 3.5 oz
Packaged on:
5/17/12
ORANGE JUICE
$2.99
Weight: 64 oz
Packaged on:
11/8/12
BEEF JERKY
$6.29
Weight: 16 oz
Packaged on:
2/24/12
PEANUT
BUTTER
$3.36
Weight: 28 oz
Packaged on:
3/24/12

31. Plain Language in the Sciences?
 25% of adults do not understand Earth orbit
 30% of adults do not understand how science works
 50% of major bills have a major scientific component
 66% of reporters cannot interpret scientific results
 Juries evaluate technical evidence (DNA-based, etc.)
 Scientists do not reach one another
 73% more citations if reported by The New York Times
 Taxpayers do not have access to publicly funded research
1 year
From National Science Foundation, National Center for Biotechnology Information& Pew Research

32.  Plain Language
… does it work?
 Satisfaction
 Safety
 Savings & Productivity
 Access
 Compliance

33. Before
After
FDA

34. LongVersion
ShortVersion

35. History
Who? When? Where?

36. Chrissie
Maher
1979

37. 1969 NYC
Consumer Affairs
1973
Bess
Myerson
You can delay enforcing any of your rights
under this note without losing them.

38. Quintilian
~70 CE
Henry V
~1420 CE
Alphonse X
~1250 CE
You should put the reader
in such a position
that he can not
misunderstand you.
2010 CE

39. PL Action & Information Network
Kathryn Catania
Katherine Spivey

40. Who?

41. Who?
“Brexit means Brexit and we’re going to make a
Titanic success of it.”
British Politician

42. Overview  Locate
 Understand
 Use
… relevant info
 Needs
 Context
 Parsing
 Coherence
Text Content
 Formatting
 Data Visualization
Text Design
 Sections
 Logical Structure
Text Flow
 Readers:
 Needs & Interests
 Writers:
 Jargon & Logic
 Topic
 Relevance
Purpose

43. Questions?
Thank you!!!
romina@languagecompass.com

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46. Unknown Author is licensed under CC BY-NC-SA
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miss-america-tarnished-by-scandal-dies-at-90/2015/01/05/4d0373a4-94fe-11e4-8005-
1924ede3e54a_story.html?utm_term=.5bf794b3f3ea
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Photo: DON HOGAN CHARLES / New York Times
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All Logos and Trademarks and the property of their respective owners and are used
in this presentation for educational purposes
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sversion.1491444014261.adapt.1900.1.jpg