Papers by Ricardo Rei
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. |
XL-Suite: Cross-Lingual Synthetic Training and Evaluation Data for Open-Ended Generation (2025.findings-emnlp)
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| Challenge: | Cross-lingual open-ended generation is an important yet understudied problem. |
| Approach: | They propose XL-Instruct, a novel technique for generating high-quality synthetic data, and introduce Xl-AlpacaEval, evaluating cross-lingual generation capabilities of large language models. |
| Outcome: | The proposed technique improves model performance by fine tuning with just 8K instructions generated using XL-Instruct, and also by improving on several fine-grained quality metrics. |
COMET: A Neural Framework for MT Evaluation (2020.emnlp-main)
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| Challenge: | Historically, metrics for evaluating the quality of machine translation (MT) have relied on basic, lexical-level features such as counting the number of matching n-grams between the MT hypothesis and the reference translation. |
| Approach: | They propose a neural framework for training multilingual machine translation evaluation models which exploits human judgements to obtain new state-of-the-art levels of correlation with MT quality. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems. |
Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral (2025.emnlp-main)
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| Challenge: | Existing quality estimation metrics are used to design effective deferral rules for machine translation. |
| Approach: | They propose a simple yet effective approach for machine translation using existing quality estimation metrics as deferral rules. |
| Outcome: | The proposed approach outperforms existing models in large translation tasks while reducing computational costs. |
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. |
TOWER+: Bridging Generality and Translation Specialization in Multilingual LLMs (2026.acl-long)
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Ricardo Rei, Nuno M Guerreiro, José Pombal, João Alves, Amin Farajian, Pedro Teixeirinha, Andre Martins
| Challenge: | Large Language Models (LLMs) are emerging as the de facto solution for multilingual machine translation. |
| Approach: | They propose a suite of LLMs that can be fine-tuned to deliver strong performance on translation and multilingual general-purpose text capabilities. |
| Outcome: | The proposed models outperform existing models on translation and general-purpose tasks. |
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. |
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages (2024.naacl-long)
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Jiayi Wang, David Adelani, Sweta Agrawal, Marek Masiak, Ricardo Rei, Eleftheria Briakou, Marine Carpuat, Xuanli He, Sofia Bourhim, Andiswa Bukula, Muhidin Mohamed, Temitayo Olatoye, Tosin Adewumi, Hamam Mokayed, Christine Mwase, Wangui Kimotho, Foutse Yuehgoh, Anuoluwapo Aremu, Jessica Ojo, Shamsuddeen Muhammad, Salomey Osei, Abdul-Hakeem Omotayo, Chiamaka Chukwuneke, Perez Ogayo, Oumaima Hourrane, Salma El Anigri, Lolwethu Ndolela, Thabiso Mangwana, Shafie Mohamed, Hassan Ayinde, Oluwabusayo Awoyomi, Lama Alkhaled, Sana Al-azzawi, Naome Etori, Millicent Ochieng, Clemencia Siro, Njoroge Kiragu, Eric Muchiri, Wangari Kimotho, Toadoum Sari Sakayo, Lyse Naomi Wamba, Daud Abolade, Simbiat Ajao, Iyanuoluwa Shode, Ricky Macharm, Ruqayya Iro, Saheed Abdullahi, Stephen Moore, Bernard Opoku, Zainab Akinjobi, Abeeb Afolabi, Nnaemeka Obiefuna, Onyekachi Ogbu, Sam Ochieng’, Verrah Otiende, Chinedu Mbonu, Yao Lu, Pontus Stenetorp
| Challenge: | Recent advances in machine translation (MT) have focused on scaling multilingual machine translation models and evaluation data to hundreds of languages, including multiple under-resourced languages. |
| Approach: | They propose to use n-gram matching metrics to measure progress in multilingual machine translation to 13 typologically diverse African languages to create high-quality human evaluation data with simplified MQM guidelines. |
| Outcome: | The proposed metrics have a higher correlation with human judgments than n-gram matching metrics such as BLEU and METEOR. |
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors (2024.findings-emnlp)
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Marcos Treviso, Nuno Guerreiro, Sweta Agrawal, Ricardo Rei, José Pombal, Tania Vaz, Helena Wu, Beatriz Silva, Daan Stigt, Andre Martins
| Challenge: | Neural machine translation systems produce translations with errors and anomalies . understanding these errors can help improve the translation quality and user experience . |
| Approach: | They propose an open large language model (LLM) built on top of TowerBase to provide free-text explanations for translation errors in order to guide the generation of a corrected translation. |
| Outcome: | The proposed model improves translation quality and user experience by allowing translators to provide free-text explanations for errors and anomalies. |
Can Automatic Metrics Assess High-Quality Translations? (2024.emnlp-main)
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| Challenge: | a recent human evaluation study found that translations produced by current MT systems achieve very high-quality scores when judged by humans on a direct assessment scale of 0 to 100. |
| Approach: | They stress-test the ability of current translation quality metrics to detect correct translations . they show that current metrics often over or underestimate translation quality . |
| Outcome: | The proposed method overestimates translation quality, the authors show . they show that current metrics often overestimate translation quality . |
Quality-Aware Decoding for Neural Machine Translation (2022.naacl-main)
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Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, Andre Martins
| Challenge: | Despite advances in machine translation quality estimation and evaluation, decoding is mostly oblivious to this. |
| Approach: | They propose to use a decoding framework that is quality-aware for neural machine translation . they compare various methods like N-best reranking and minimum Bayes risk decoding . |
| Outcome: | The proposed quality-aware decoding outperforms MAP-based decoding on four datasets and two model classes. |
Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning (2023.findings-emnlp)
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Duarte Alves, Nuno Guerreiro, João Alves, José Pombal, Ricardo Rei, José de Souza, Pierre Colombo, Andre Martins
| Challenge: | Large language models (LLMs) are a promising avenue for machine translation (MT) however, their effectiveness depends on the choice of few-shot examples and they often require extra post-processing due to overgeneration. |
| Approach: | They propose a method that incorporates few-shot examples during finetuning to improve performance on MT tasks. |
| Outcome: | The proposed method outperforms few-shot prompting while eliminating the need for in-context examples. |
Uncertainty-Aware Machine Translation Evaluation (2021.findings-emnlp)
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| Challenge: | Several neural-based metrics have been proposed to evaluate machine translation quality, but they are trained on noisy, biased and scarce human judgements. |
| Approach: | They propose a method to evaluate machine translation quality using point estimates . they combine COMET framework with Monte Carlo dropout and deep ensembles . |
| Outcome: | The proposed methods perform well across multiple language pairs and with references. |
Disentangling Uncertainty in Machine Translation Evaluation (2022.emnlp-main)
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| Challenge: | Trainable evaluation metrics for machine translation (MT) exhibit strong correlation with human judgements, but they are often hard to interpret and might produce unreliable scores under noisy or out-of-domain data. |
| Approach: | They propose to use Monte Carlo dropout and deep ensembles to quantify uncertainty in machine translation and assess their ability to target different sources of aleatoric and epistemic uncertainty. |
| Outcome: | The proposed measures can target different sources of aleatoric and epistemic uncertainty, with a reduction in computational costs. |
WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages & Dialects (2025.findings-acl)
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Daniel Deutsch, Eleftheria Briakou, Isaac Rayburn Caswell, Mara Finkelstein, Rebecca Galor, Juraj Juraska, Geza Kovacs, Alison Lui, Ricardo Rei, Jason Riesa, Shruti Rijhwani, Parker Riley, Elizabeth Salesky, Firas Trabelsi, Stephanie Winkler, Biao Zhang, Markus Freitag
| Challenge: | In order to evaluate large language models (LLMs), it is important to collect benchmark datasets in order to assess their multilingual performance. |
| Approach: | They extend the WMT24 dataset to cover 55 languages by collecting new human-written references and post-edits for 46 new languages/dialects. |
| Outcome: | The proposed dataset covers 55 languages and provides best-performing MT systems in all 55 languages. |
Multilingual Email Zoning (2021.eacl-srw)
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| Challenge: | Existing literature on email zoning is mainly limited to English . however, it is possible to discern a level of formal organization in the way most emails are formed. |
| Approach: | They propose a multilingual email zoning benchmark based on a language agnostic sentence encoder and a new model that uses a biLSTM with a CRF to classify each sentence into an email zone. |
| Outcome: | The proposed model is competitive with current English benchmarks and reached state-of-the-art performance in English. |
Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation (2024.emnlp-main)
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Sweta Agrawal, José De Souza, Ricardo Rei, António Farinhas, Gonçalo Faria, Patrick Fernandes, Nuno Guerreiro, Andre Martins
| Challenge: | Existing algorithms for machine translation do not match human preferences, but they can be expensive to obtain and curate at a large scale. |
| Approach: | They propose an approach that leverages the best of both worlds by collecting sentence-level quality assessments from professional linguists on translations generated by multiple high-quality MT systems. |
| Outcome: | The proposed approach improves translation quality on WMT23 and FLORES benchmarks. |