New Interactions for Users of Machine Translation

M2 Internship

November 18, 2024

Keywords: Multilingual Large Language Models, Post-Edition, Confidence Estima-
tion

Research Context and Questions

Machine Translation (MT) technologies are routinely used in a
multitude of contexts for a variety of tasks. In most cases, MT users
are amateur translators, who have not been specifically trained to use
MT tools, are mostly unaware of their strengths and weaknesses, and
who, contrarily to professional translators, may only have a partial
command (in the extreme case no command at all) of the source or the
target language. In most cases, the translation is not the final
product, rather a convenient way to achieve some communicative task -
the translation need not be perfect, but should be fit for its
intended purpose. Yet, existing online translation interfaces provide
little help to achieve such goals: in most cases, it consists of two
windows, one where the source text is entered, and the second where
the target text is generated.

In this research we aim to explore ways to provide novel
visualisations and interaction techniques for lay users of MT,
focusing on ways to assist writers to identify the parts that are
reliably translated, and to act on the others so as to improve the
translation. We plan, for instance, to investigate the following
set-ups:

Pre-edition

Alice writes a message M in his mother tongue LA, and automatically
translates it before sending TA->B (M ) to Bob (in a language LB that
she does not necessarily read). In this situation, translating TA->B (M
) backwards into LA may help Alice to spot in TB->A(TA->B (M ))
fragments whose trans- lation are suspicious. By iteratively
pre-editing (Miyata and Fujita, 2021) the message M so that TB->A(TA->B
(M )) gets closer to M , Alice may eventually get more confident that
what Bob gets actually correspond to the meaning of M (Zouhar and
Bojar, 2020). Can we guide Alice to perform this task more
effectively, e.g. by highlighting the differences, by also using
automatic confidence measures (Specia et al., 2017; Rei et al., 2022)
or explanation generation techniques (Stahlberg et al., 2018; Vamvas
and Sennrich, 2022; Briakou et al., 2023; Sennrich et al., 2024)?
Assuming that Alice wants some parts of the 1 translation (e.g. terms,
or places, or dates, or amounts) to be 100% correct, can we leverage
existing resources to increase her confidence in the translation?

[Post-edition] Bob conversely needs to produce a publishable message
in L2 (a language he masters), based on an initial version in L1 (a
language that he may not know), that he received from Alice. After
computing an automatic translation, Bob can revise the text to improve
the style or content. Again, can we help Bob so that he can focus on
fixing the most problematic parts of the translation, e.g. with
confidence estimation Guerreiro et al. (2024)? Can we provide Bob
additional interactions, such as the ability to correct multiple
repeated errors in just one edit? Can we help Bob perform time
vs. quality tradeoffs, so that the translation effort is used
optimally?

This internship will explore topics at the intersection of natural
language pro- cessing (NLP) and human-computer interaction (HCI). In
the first stage, the intern will assimilate the state of the art in
this nascent field. They will then focus on one setup (pre- or
post-edition), develop an experimental platform, and run small-scale
experiments with actual MT users.

Internship conditions

The internship will be jointly supervised by Marine Carpuat and
François Yvon, with the help of Gilles Bailly (ISIR). The internship
will take place in the MLIA team of ISIR3. ISIR is under the dual
supervision of Sorbonne Université, a world-class multidisciplinary
university, and the French National Centre for Scientific Research
(CNRS), one of the most important research institutions in the
world. ISIR includes 6 research teams and 226 people. The intern will
be located at 4, place Jussieu, 75005 Paris.

- Remuneration: around 600C along with the refund of half the Navigo
(public transport) card.

- Starting date: the internship is expected to start in February or March 2025.

- Duration: 5-6 months.

Requirements

We are looking for a second-year Master's student with a strong
background in Natural Language Processing, Machine Learning or
Human-Computing Interaction. The intern is expected to be proficient
in programming, especially in the Python language, and to have already
worked under Linux. They should also have experience with a deep
learning framework, preferably PyTorch.

Application

Please send a resume along with a cover letter (in French or English)
and grade transcripts for the last two years to François Yvon at
yvon@isir.upmc.fr. A list of pointers to example projects (e.g., via
GitHub) is a plus.

References

Eleftheria Briakou, Navita Goyal, and Marine Carpuat. 2023. Explaining
with contrastive phrasal highlighting: A case study in assisting
humans to detect translation differences. In Proceedings of the 2023
Conference on Empirical Methods in Natural Language Processing, pages
11220-11237, Singapore. Association for Computational Linguistics.

Nuno M. Guerreiro, Ricardo Rei, Daan van Stigt, Luisa Coheur, Pierre
Colombo, and André F. T. Martins. 2024. xcomet: Transparent machine
translation evaluation through fine-grained error
detection. Transactions of the Association for Computational
Linguistics, 12:979-995.

Rei Miyata and Atsushi Fujita. 2021. Understanding pre-editing for
black-box neural machine translation. In Proceedings of the 16th
Conference of the European Chapter of the Association for
Computational Linguistics: Main Volume, pages 1539-1550,
Online. Association for Computational Linguistics.

Ricardo Rei, Marcos Treviso, Nuno M. Guerreiro, Chrysoula Zerva, Ana C
Farinha, Christine Maroti, José G. C. de Souza, Taisiya Glushkova,
Duarte Alves, Luisa Coheur, Alon Lavie, and André
F. T. Martins. 2022. CometKiwi: IST-unbabel 2022 submission for the
quality estimation shared task. In Proceedings of the Seventh
Conference on Machine Translation (WMT), pages 634-645, Abu Dhabi,
United Arab Emirates (Hybrid). Association for Computational
Linguistics.

Rico Sennrich, Jannis Vamvas, and Alireza
Mohammadshahi. 2024. Mitigating hallucinations and off-target machine
translation with source-contrastive and language-contrastive
decoding. In Proceedings of the 18th Conference of the European
Chapter of the Association for Computational Linguistics (Volume 2:
Short Papers), pages 21-33, St. Julian's, Malta. Association for
Computational Linguistics.

Lucia Specia, Carolina Scarton, and Gustavo Henrique
Paetzold. 2017. Quality estimation for Machine Translation. Synthesis
Lectures on Human Language Technologies. Morgan & Claypool Publishers.
Felix Stahlberg, Danielle Saunders, and Bill Byrne. 2018. An operation
sequence model for explainable neural machine translation. In
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and
Interpreting Neural Networks for NLP, pages 175-186, Brussels,
Belgium. Association for Computational Linguistics.


Jannis Vamvas and Rico Sennrich. 2022. As little as possible, as much
as necessary: Detecting over- and undertranslations with contrastive
conditioning. In Proceedings of the 60th Annual Meeting of the
Association for Computational Linguistics (Volume 2: Short Papers),
pages 490-500, Dublin, Ireland. Association for Computational
Linguistics.

Vilém Zouhar and Ondrej Bojar. 2020. Outbound translation user
interface ptakopet: A pilot study.  In Proceedings of the Twelfth
Language Resources and Evaluation Conference, pages 6967-6975,
Marseille, France. European Language Resources Association.