Machine Translation andScholarly Communication
Lynne Bowker (lbowker@uottawa.ca)

School of Translation & Interpretation and School of Information Studies,
University of Ottawa, Canada
NAWA Visiting Researcher, Scholarly Communication Research Group,
Adam Mickiewicz University, PolanduOttawa.ca

Helsinki Initiative Webinar on Multilingual Scholarly Communication 31/5/23

Machine translation• Long history, linguistic approaches• Slow, low quality, not widely used

• Post 1990s• Fast, powerful, cheaper computers• Digital document production• Internet• Data-driven approaches to MT• Vast improvement in translation
quality, but still not problem-free(Image credit: Pixabay.com)

Data-driven approaches• Need HUGE quantities of data
But also• The right kind of data
• Generative, derivative• New content based entirely on examples intraining corpus• High-resource and low(er)-resource
situations• Think of the “hands” problem with AIgenerated images• Fewer examples, many variations(Image credit: Pixabay.com)

High- and low-resource situations
High-resource
language

Lower-resource
language

High-resource
domain/text type• English computer
user manuals• French recipes

Lower-resource
domain/text type• English texts onreindeer husbandry• French texts oncricket• Polish administrativetexts• Finnish texts on
telecommunications• Polish nuclear reactor
maintenance manuals• Finnish texts on dengue
feverTranslation requires a bilingual parallel corpus• Need a lot of translation activity
between a given pair oflanguages• High-resource languages• Russian and Bengali bothhave ~250 million speakers
BUT• Relatively little translation
activity between them = lowresource language pair
• Language varieties• Less dominant varieties get furthermarginalizedApproaches to overcoming low-resource
situations• Pivot through anotherlanguage• Twice as many opportunities
to make errors• Errors in step 1 carriedforward into step 2• Make synthetic data
• Using MT to translate andfeeding the results back into
the training corpus• Lower quality input(Image credits: Pixabay.com)

Machine translation & scholarly communication• Challenge• Many languages and domains are low(er)resourced• What can authors do?
• Produce accessible resources in other languages• Open access• Multilingual abstracts• Science communication (plain language summaries)• Improve their MT literacy• Understanding how data-driven MT works• Translation friendly writing(Image credit: Pixabay.com)

Translation friendly writing• Applies to any language• Specific techniques may be language
dependent• Quality of the input text has a major
impact on translation quality• Authors of the original text can make iteasier for others to read their work throughan additional language or via MT• “Garbage in, garbage out” (GIGO)• Write clearly and unambiguously(Image credit: Pixabay.com)

Writing for (machine) translation• Skilled human translators cancompensate• Fix spelling, grammar or punctuation errors• Make educated guesses• Refer to other parts of the text for context• MT tools cannot compensate• Process the character string as presented• Have no real-world knowledge• Process each sentence as a separate unit
(Image credit: Pixabay.com)

Sample tips (not language specific)• Use spelling and grammar checkers• Ensure sentences are not too long… or
too short (~10-25 words)• Use terminology consistently• Minimize acronyms, abbreviations• Minimize pronouns• Avoid using all caps
• Avoid unnecessary line breaks(Image credit: Pixabay.com)

Using other AI tools to support MT• ChatGPT-4 (using a LLM for MT)• Underperforms for low-resource
languages, distant pairs• Prompting is important• “You are a machine translation system”• Task- and domain-specific prompts• Adjusting “temperature”• High (1) = more variety, more potential forerrors (responses to the same prompt vary)• Low (0) = more conservative, predictable (getthe same response given the same prompt)(Image credit: OpenAI.com)

Other language-related tasks for AI• REMEMBER: Translation is a hugely
complex task
• Other AI tools can help you to get yourtext in shape for MT• Paraphrasing, e.g. ParaphraseTool• Simplification, e.g. Simplif.ai• Summarization, e.g. Genei• Other tools• Plain language checkers (e.g. Hemingway)• Readability checkers (e.g. in MS Word)• Read-aloud features (e.g. in MS Word)(Image credit: Pixabay.com)

Other issues?
• Ethical considerations• Evolving landscape of attitudes andpractices• Distinction between content generationand linguistic support• Need for more nuanced policies, notsimple bans

(Image credit: Pixabay.com)

Beyond authors?
• How else can MT help?
• Knowledge discovery• Using MT to translate metadata for cross-language
information retrieval• Knowledge assimilation• Translation-friendly writing + MT into your dominantlanguage: your linguistic and domain knowledge
should help you to compensate for some translation
issues• Multilingual subtitles (speech generation + MT)• Writing in other languages• Combined with other tools, MT can be used
iteratively as a 2nd-language writing aid(Image credit: Pixabay.com)

Comments or questions
lbowker@uottawa.ca(Image credit: Pixabay.com)

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