Papers by António Farinhas
Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral (2025.emnlp-main)
Copied to clipboard
| 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. |
Can Automatic Metrics Assess High-Quality Translations? (2024.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
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. |
An Empirical Study of Translation Hypothesis Ensembling with Large Language Models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are becoming a one-fits-many solution, but they sometimes hallucinate or produce unreliable output. |
| Approach: | They propose to use several LLMs to ensemble translation hypotheses . they use instruction tuning, quality-based reranking, and minimum Bayes risk (MBR) decoding to improve translation quality. |
| Outcome: | The proposed method improves translation quality and instruction tuning improves the quality of the output. |
Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation (2024.emnlp-main)
Copied to clipboard
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. |