--- title: "André F. T. Martins — papers, page 2 · University of Lisbon" description: "André F. T. Martins, University of Lisbon: 35 papers on Elamur Research (2010–2026), h-index 39 — affiliations, topics, co-authors and who…" canonical: "https://research.elamur.ai/free/en/authors/andre-f-t-martins/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/andre-f-t-martins/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [André F. T. Martins](https://md.research.elamur.ai/free/en/authors/andre-f-t-martins/) / page 2 # André F. T. Martins · page 2 35 papers in our library · 2010–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/andre-f-t-martins/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/andre-f-t-martins/) [1](https://md.research.elamur.ai/free/en/authors/andre-f-t-martins/) **2** 26. **[Findings of the WMT 2021 Shared Task on Quality Estimation](https://md.research.elamur.ai/free/en/papers/specia-2021-findings-wmt-2021-shared-task/)** *(2021 · 80 citations)* Quality Estimation (QE) predicts how good a machine translation is without access to a reference translation. The 2021 edition of the WMT QE shared task ran three subtasks on output from neural MT systems: 27. **[MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset](https://md.research.elamur.ai/free/en/papers/fomicheva-2022-mlqe-pe-multilingual-quality-estimation/)** *(2022 · 76 citations)* MLQE-PE is a public dataset for training and testing machine translation Quality Estimation (QE), which predicts how good a translation is without a reference. 28. **[Turbo Parsers: Dependency Parsing by Approximate Variational Inference](https://md.research.elamur.ai/free/en/papers/martins-2010-turbo-parsers-dependency-parsing-approximate/)** *(2010 · 69 citations)* Rich non-projective dependency parsers that score sibling and grandparent arcs make exact inference intractable, so they need approximate decoding. 29. **[MaLA-500: Massive Language Adaptation of Large Language Models](https://md.research.elamur.ai/free/en/papers/lin-2024-mala-500-massive-language-adaptation/)** *(2024 · 58 citations)* Most large language models are trained mainly on English or a few dozen languages, so they perform poorly on low-resource languages. 30. **[Transferring Coreference Resolvers with Posterior Regularization](https://md.research.elamur.ai/free/en/papers/martins-2015-transferring-coreference-resolvers-posterior-regularization/)** *(2015 · 35 citations)* Coreference resolvers usually depend on annotated corpora, which most languages lack. This paper builds resolvers for low-resource languages by transferring knowledge from an English system, using English→Spanish and English→Brazilian Portuguese as test cases. First, the Berkeley Coreference Resolution system labels… 31. **[EuroBERT: Scaling Multilingual Encoders for European Languages](https://md.research.elamur.ai/free/en/papers/boizard-2025-eurobert-scaling-multilingual-encoders-european/)** *(2025 · 34 citations)* Bidirectional encoders still power most retrieval, classification and regression systems, but recent modeling progress has mostly gone into decoder-only LLMs. 32. **[Findings of the WMT 2020 Shared Task on Chat Translation](https://md.research.elamur.ai/free/en/papers/farajian-2020-findings-wmt-2020-shared-task/)** *(2020 · 29 citations)* This report presents the inaugural WMT competition devoted to translating chat. Its focus is bilingual customer-support conversations in which a German-speaking customer and an English-speaking agent each write in their own language. Such chat text is short, informal, noisy and heavy in pronouns, and public data for… 33. **[Assessing the Role of Context in Chat Translation Evaluation: Is Context Helpful and Under What Conditions?](https://md.research.elamur.ai/free/en/papers/agrawal-2024-assessing-role-context-chat-translation/)** *(2024 · 8 citations)* Most automatic MT metrics were built and validated on well-formed text such as news. It is unclear how well they judge machine-translated chats, which are short, informal and depend heavily on earlier turns. The authors first compare MQM error annotations for chat and news in WMT22 English–German. Errors in chat turn… 34. **[Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings](https://md.research.elamur.ai/free/en/papers/ramos-2026-fine-grained-reward-optimization-machine/)** *(2026 · 7 citations)* Most RL training for neural MT scores a translation with one number per sentence. This sparse signal does not say where errors occur, so learning is slow and unstable, especially for long inputs. The authors instead derive a reward for each token from XCOMET, a quality estimation model that marks error spans and… 35. **[A Context-aware Framework for Translation-mediated Conversations](https://md.research.elamur.ai/free/en/papers/pombal-2026-context-aware-framework-translation-mediated/)** *(2026 · 4 citations)* Machine translation can connect people, or people and English-centric assistants, who lack a shared language. 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