Papers with quality-aware decoding
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. |
Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decoding (2026.eacl-long)
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| Challenge: | Large language models (LLMs) struggle to adequately handle discourse phenomena, such as pronoun resolution and lexical cohesion at the document level. |
| Approach: | They propose to use minimum Bayes risk decoding to extract discourse knowledge from LLMs and propose to apply QAD to enhance the semantic richness of translations. |
| Outcome: | The proposed method outperforms other methods and enhances the semantic richness of translations and aligns them more closely with human preferences. |
Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation (2025.acl-long)
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| Challenge: | Qualitative estimation (QE) metrics have been optimized to align with human quality judgments, but whether they encode social biases has been largely overlooked. |
| Approach: | They define and investigate gender bias of QE metrics and discuss its downstream implications for machine translation (MT) when a human entity’s gender in the source is undisclosed, masculine-inflected translations score higher than feminine-infflectes translations are penalized. |
| Outcome: | The proposed measures are based on gender-based quality estimation metrics across multiple domains, datasets, and languages. |