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Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decoding

Paper · 2026 · Wafaa Mohammed, Vlad Niculae and Chrysoula Zerva

Summary

LLM-based translation still often fails on document-level discourse. Typical errors include inconsistent repeated terms, wrong pronoun gender, shifting formality and incorrect verb forms. The authors suspect the models do hold this knowledge, but greedy decoding fails to bring it out. They test this by comparing greedy outputs against quality-aware decoding (QAD), mainly minimum Bayes risk (MBR) decoding over sampled candidates. The models are TowerInstruct-13B, EuroLLM-9B-Inst, Gemma3-12B-it and Qwen3-14B, with NLLB-3.3B as an encoder-decoder baseline. Phenomena are tagged automatically with MuDA. Evaluation uses the discourse-rich DELA corpus, plus TED2020 and WMT24++ across six English-source directions. Under greedy decoding, the encoder-decoder system handles discourse better than the LLMs. With MBR, the LLMs overtake it, according to automatic metrics, LLM-as-a-judge and human assessment. The selected translations also carry richer meaning and match human preferences more closely. The work suggests that better inference alone, without retraining, can recover discourse competence in LLMs. It also contributes human-annotated discourse data for future research.

Key points

  • Four phenomena studied (following Fernandes et al., 2023): lexical repetition, pronoun resolution, formality and verb form. All are for EN→XX translation, with targets PT (Brazilian), DE, FR, KO, AR and RU.
  • Data:
    • DELA: 60 documents, about 3.7K sentences, EN–PT, from multiple domains.
    • TED2020 subset: about 20K sentences in roughly 160 documents, taken from OPUS.
    • WMT24++: about 1K sentences across 169 documents.
  • Decoding: candidates are generated with nucleus sampling (p=0.9) and the best one is chosen by MBR.
  • Ablations:
    • MBR with lexical, pretrained and discourse-specific utility metrics.
    • Automatic post-editing.
    • Sample fusion.
  • Released resources: human discourse annotations built on TED2020, with code and data at github.com/Wafaa014/Discourse_translation_in_LLMs.
  • Stated limitations:
    • MuDA misses some phenomena, such as zero pronouns.
    • Only one sampling setup was tested.
    • The human evaluation covers a limited amount of data.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of AmsterdamAmsterdam · Netherlands144 works · 173,906 citations🔬Instituto de TelecomunicaçõesLisbon · Portugal35 works · 5,247 citations🎓Instituto Superior TécnicoLisbon · Portugal32 works · 4,064 citations🎓University of LisbonLisbon · Portugal26 works · 3,589 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Discourse analysis · 466 works

Cite

Cite as Wafaa Mohammed, Vlad Niculae and Chrysoula Zerva. 2026. Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decoding. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.eacl-long.220

@inproceedings{mohammed2026unlocking,
  title = {{Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decoding}},
  author = {Wafaa Mohammed, Vlad Niculae and Chrysoula Zerva},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.220},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.220}
}

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