The ATA Chronicle - January/February 2021 - 35

RR

word or phrase translation

Samuel! It feels like we could

alternatives presented in a

continue talking about this

NOTES
1
Zetzsche, Jost. " Women and Machine Translation, "
The ATA Chronicle (November/December 2020), 25,
http://bit.ly/women-MT.

dropdown menu. The latter

for a long time. But it seems

2

were used so rarely that

even more important at this

they didn't make it into the

point that translators start

You can find Nico Herbig on Twitter at
https://twitter.com/nico_herbig.

3

Samuel Läubli is on Twitter as well:
https://twitter.com/samlaeubli.

4

O'Hagan, Minako (Editor). Routledge Handbook of
Translation and Technology (Routledge, 2020),
https://bit.ly/Routledge-translation.

5

Sennrich, Rico, Barry Haddow, and Alexandra Birch.
" Controlling Politeness in Neural Machine Translation
via Side Constraints. " In the Proceedings of the 2016
Conference of the North American Chapter of the
Association for Computational Linguistics: Human
Language Technologies (Association for Computational
Linguistics, 2016), http://bit.ly/politeness-NMT.

6

Bulte, Bram, and Arda Tezcan. " Neural Fuzzy Repair:
Integrating Fuzzy Matches into Neural Machine
Translation. " In the Proceedings of the 57th Annual
Meeting of the Association for Computational Linguistics
(Association for Computational Linguistics, 2019),
http://bit.ly/fuzzy-matches.

7

Sennrich, Rico, Barry Haddow, and Alexandra Birch.
" Controlling Politeness in Neural Machine Translation
via Side Constraints. " In the Proceedings of the 2016
Conference of the North American Chapter of the
Association for Computational Linguistics: Human
Language Technologies (Association for Computational
Linguistics, 2016), http://bit.ly/politeness-NMT.

8

Bulte, Bram, and Arda Tezcan. " Neural Fuzzy Repair:
Integrating Fuzzy Matches into Neural Machine
Translation. " In the Proceedings of the 57th Annual
Meeting of the Association for Computational Linguistics
(Association for Computational Linguistics, 2019),
http://bit.ly/fuzzy-matches.

9

Herbig, Nico. " The Evolving Translator-Computer
Interface, " eMpTy Pages (October 21, 2020),
http://bit.ly/Herbig-eMpTy-Pages.

10

Zetzsche, Jost. " Getting Physical, " The ATA Chronicle
(August 2013), 29, http://bit.ly/getting-physical.

11

Albarino, Seyma. " New Research Flips the Script
on CAT Tools-Literally, " Slator (November 25, 2020),
http://bit.ly/Slator-CAT.

12

Ashwin K., Michael Cogswell, Ramprasath R. Selvaraju,
Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra.
" Diverse Beam Search: Decoding Diverse Solutions from
Neural Sequence Models, " http://bit.ly/diverse-beam-search.

user's input, rendered as
ghost text-and multiple

final product.
However, regardless of
how suggestions will be
visualized, it's vital that they
combine all the resources
available to translators: TMs,
TBs, and MT. Jost, I like your
idea of using multiple MT
engines for auto-suggest.
At TextShuttle, we're using
a technique called Diverse
Beam Search12 to produce
diverse translation variants
with a single engine. The
rationale is that even MT
engines from different
providers typically produce
very similar translations
for many sentences. And
because NMT systems
always generate multiple
translation variants behind
the scenes as they generate
a target sentence, enforcing
variability comes with almost
no computational overhead.
It's easy to generate multiple
translation variants for a

Jost

Thank you so much
for this, Nico and

considering some of the
things we've discussed, and
that tool developers start a
dialogue among themselves
to see whether they can
implement some of the
changes mentioned here. Or
maybe there's even a team
of developers that will read
this and say, " Yeah, there
are so many good ideas in
this that I think I can build
something new and
interesting and become
super-rich selling it! "
(Well, the latter is not
going to happen, but the
former might).
My biggest take away from
our discussion is that just
because we think we've
found a widely accepted way
of working, doesn't mean
that we couldn't and
shouldn't be questioning it
on a continuous basis and,
well, making it better. All this
excites me greatly, partly
because it goes to show that

given source sentence with

professional technical

a single NMT system, but as

translation (and by

long as CAT tools don't query

" technical " I mean to include

and visualize them, there's

virtually everything non-

no way for professional

literary) is alive and well,

translators to take advantage

even as research tries to find

of them.

better ways to facilitate it.

Jost Zetzsche, CT is chair of ATA's Translation
and Interpreting Resources Committee. He is the
author of Translation Matters, a collection of 81
essays about translators and translation technology.
jzetzsche@internationalwriters.com
Remember, if you have any ideas and/or suggestions regarding
helpful resources or tools you would like to see featured, please
email Jost Zetzsche at jzetzsche@internationalwriters.com.
www.ata-chronicle.online

American Translators Association

35


http://www.bit.ly/women-MT https://www.twitter.com/nico_herbig https://www.twitter.com/samlaeubli https://www.bit.ly/Routledge-translation http://www.bit.ly/politeness-NMT http://www.bit.ly/fuzzy-matches http://www.bit.ly/politeness-NMT http://www.bit.ly/fuzzy-matches http://www.bit.ly/Herbig-eMpTy-Pages http://www.bit.ly/getting-physical http://www.bit.ly/Slator-CAT http://www.bit.ly/diverse-beam-search http://www.ata-chronicle.online

The ATA Chronicle - January/February 2021

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