Language technologies for supporting
multilingual scholarly communication
Lynne Bowker (lbowker@uottawa.ca)
School of Translation & Interpretation

uOttawa.ca

Helsinki Initiative 5th Anniversary, 3 April 2024

Major changes in technological landscape• 1940s to 1990s• 1990s to present• MT = linguistic, rule-based• MT = data-driven• Computers were brandnew (enormous, limited
power, speed, storage)• Few people had one• Stand-alone• Computers are ubiquitous(small, fast, powerful, lots ofstorage)• Everyone creates digital text
• Networked (internet, www,intranets), easy to access/share

What is data-driven? Machine learning?

Da t a !
(fu e l)Image credit: Pixabay.com

Training data = examples (for machine learning)
1) Identify a task for the AI tool
• e.g. image classification, translation

2) Show the tool examples of what
you want it to learn• e.g. photos of two different types ofanimals, previously translated texts

3) Give the tool some feedback (e.g.
confirm correct answers)
4) Test the tool on new data that it
hasn’t seen before

Narrow vs general tasks
MANY, MANY examples• (Enough fuel)High quality examples• (The right kind of fuel)

AI is not smart• It can process data, but it doesn’t
understand itData-driven approaches have strengths• Free versions available• Convenience (24/7)• Fluent (sounds good)• Able to learn (patterns)• Work well for highresource languages,domains and text types

But also limitations• Hallucinations• Only does pattern matching + counting (no understanding)• Data-driven = data-sensitive (e.g. bias, including lang variety)• Perform less well for low-resource languages/pairs, domains
and text types• Google Translate = 134 languages (/7000+)• “No Language Left Behind” = 200 languages• Overwhelming use of EN for scholarly communication means somelanguages don’t have well developed scientific terminology• Specialized content = lower volume• Paywalled content = lower volume• Open access is important!

Image credit:
Pixabay.com

Other costs• Extremely computationally intensive• Only large corporations can afford todevelop, train and fine tune very large-scalemodels• Determines who can and cannot participate• May not remain freely accessible forever• Not environmentally friendly• Training one model = carbon footprint of 6cars• We have work to do to develop more
efficient algorithms, SML, etc.

Image credit: Pixabay.com

NMT• Dedicated to translation• Parallel corpora (equalamount of data in bothlanguages)• May pivot through EN or
another language, but notas often• *Usually* a better choice
when the task istranslation (esp. for lowresource situations)vsLLM• Multi-use tool

• Q&A, summarization, paraphrasing,translation• Often unbalanced language
resources (e.g. 90% of ChatGPT’s
corpus is in EN, remaining 10%
covers all other languages)• Often pivots through EN behind thescenes• EN language also equals EN (US)worldview

Garbage in, garbage out!
• Quality of input text affects
quality of translated text

• Well-written input (plainlanguage) is more translatable• We can ALL work to craft
clearer input (reader- andtranslation-friendly writing,intralingual translation)• Plain language summaries• *NOTE: post-editing will likely
still be necessary• FREE resources on
Machine Translation
Literacy Project site(>>Teaching Resources)• https://sites.google.com/
view/machinetranslation
literacy/MT can help… but MT alone is NOT sufficient• Policies to value and promote
multilingual publishing• Multilingual metadata to support
discovery• MT better suited to support reading
work in other languages, rather thanwriting it• Human-computer interaction• OPERAS, CLF• Beyond published articles (slides,posters, presentations)?

Bowker, Ayeni & Kulczycki(2023)• Systematic review ofliterature at theintersection of translation
technology and scholarly
communication
https://doi.org/10.20381/85
8s-q632

lbowker@uottawa.ca(Image credits: Pixabay.com)