Papers by Luisa Coheur

6 papers
MT-Telescope: An interactive platform for contrastive evaluation of MT systems (2021.acl-demo)

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Challenge: MT-Telescope is an open source, written in Python, and is built around a user friendly and dynamic web interface.
Approach: They propose a platform to facilitate comparative analysis of the output quality of two Machine Translation (MT) systems.
Outcome: The proposed platform supports fine-grained segment-level analysis and interactive visualisations that expose the fundamental differences in the performance of the compared systems.
A Rewriting Approach for Gender Inclusivity in Portuguese (2023.findings-emnlp)

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Challenge: Recent research has focused on gender-inclusive and gender-neutral language . however, current approaches to gender- neutral rewriting for gendered languages rely on large datasets .
Approach: They propose a rule-based and a neural-based tool for gender-neutral rewriting for Portuguese, a heavily gendered Romance language.
Outcome: The proposed model fine-tunes large multilingual machine translation models on examples generated by the rule-based model.
Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort (2021.acl-long)

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Challenge: Existing methods to evaluate multiple systems are expensive and require human evaluators.
Approach: They propose a novel online learning approach that dynamically converges to the top-3 ranked systems for the language pairs considered by taking advantage of human feedback.
Outcome: The proposed approach converges to the top-3 ranked systems for the language pairs considered despite the lack of human feedback for many translations.
Enhancing Accessible Communication: from European Portuguese to Portuguese Sign Language (2023.findings-emnlp)

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Challenge: Existing systems for translating European Portuguese into LGP glosses rely on hand-crafted rules . current systems rely only on toy examples, disregarding non-manual movements .
Approach: They propose a corpora-driven rule-based machine translation system between European Portuguese and LGP glosses and two neural machine translation models.
Outcome: The proposed system improves on existing translation systems and annotates a gold collection of the results.
The Inside Story: Towards Better Understanding of Machine Translation Neural Evaluation Metrics (2023.acl-short)

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Challenge: Neural metrics for machine translation evaluation are considered "black boxes" lexical overlap-based metrics are popular for evaluation of translation systems and algorithms .
Approach: They develop and compare several neural explainability methods to understand translation errors . they aim to better understand the correspondence between token-level explanations and human annotated error spans .
Outcome: The proposed methods leverage token-level information that can be directly attributed to translation errors.
AIA-BDE: A Corpus of FAQs in Portuguese and their Variations (2020.lrec-1)

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Challenge: a corpus of 380 domain-oriented FAQs in Portuguese is presented . paraphrases or entailed questions are created manually, by humans, or automatically, with Google Translate.
Approach: They present a corpus of 380 domain-oriented FAQs in Portuguese and their variations, i.e., paraphrases or entailed questions, created manually, by humans, or automatically, with Google Translate.
Outcome: The proposed system outperforms other systems in the domain of question-answering . it performs well when matching variations with their original questions .

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