--- title: "Machine translation — paper cards" description: "Machine translation: 310 paper cards of the Elamur Research library · page 1 of 13." canonical: "https://research.elamur.ai/free/en/shelves/mtrans/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/mtrans/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / Machine translation # Machine translation 310 cards · page 1 of 13 · newest first · [Linguistics](https://md.research.elamur.ai/free/en/shelves/linguistics/) · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Interlingua and transfer, tree-to-tree, rule-based MT, generation, style and Bible translation, evaluation. Mova translates without a neural network at answer time: a recogniser of the language, a tree, frames, a world, then a meta-world shared by all languages and authors, and back down through the standard stack of the target language, with each author as a small delta over it. These cards cover the work this rests on: interlingua and transfer, tree-to-tree translation and synchronous grammars, rule-based systems, generation from trees and meaning, style and author transfer, Bible translation, and how translation is measured. Each card names the ideas we take for the translator and what they did on the verse-aligned Bibles, where every block must come back byte for byte. **1** [2](https://md.research.elamur.ai/free/en/shelves/mtrans/page/2/) … [13](https://md.research.elamur.ai/free/en/shelves/mtrans/page/13/) [next →](https://md.research.elamur.ai/free/en/shelves/mtrans/page/2/) - [Letter 57, to Pammachius, On the Best Method of Translating](https://md.research.elamur.ai/free/en/papers/jerome-1893-letter-57-pammachius-best/) — Jerome, W. H. Fremantle · 395 - [AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing](https://md.research.elamur.ai/free/en/papers/thillainathan-2026-authormix-modular-authorship-style-transfer/) — Sarubi Thillainathan, Ji-Ung Lee, Michael Sullivan, et al. · 2026 - [First Shared Task on UMR Parsing](https://md.research.elamur.ai/free/en/papers/tepanek-2026-first-shared-task-umr-parsing/) — Jan Štěpánek, Daniel Zeman, Marketa Lopatkova, et al. · 2026 - [From Syntax to Semantics: Introducing UMR for NLP Annotation](https://md.research.elamur.ai/free/en/papers/pagano-2026-syntax-semantics-introducing-umr-nlp/) — Adriana S. Pagano, Magali Sanches Duran, Federica Gamba · 2026 - [Modular Neural Machine Translation with a Semantic Pivot - Pilot Study Using AMR](https://md.research.elamur.ai/free/en/papers/gao-2026-modular-neural-machine-translation-semantic/) — Wenyang Gao, Yaxuan Li, Yunxin Bao, et al. · 2026 - [Syntax is the Key to Semantics: Combining Universal Dependencies and Abstract Meaning Representation](https://md.research.elamur.ai/free/en/papers/heinecke-2026-syntax-is-key-semantics-combining/) — Johannes Heinecke · 2026 - [Word Alignment-Based Evaluation of Uniform Meaning Representations](https://md.research.elamur.ai/free/en/papers/zeman-2026-word-alignment-based-evaluation-uniform/) — Daniel Zeman, Federica Gamba · 2026 - [Bootstrapping UMRs from Universal Dependencies for Scalable Multilingual Annotation](https://md.research.elamur.ai/free/en/papers/gamba-2025-bootstrapping-umrs-universal-dependencies-scalable/) — Federica Gamba, Alexis Palmer, Daniel Zeman · 2025 - [Exploiting Task Reversibility of DRS Parsing and Generation: Challenges and Insights from a Multi-lingual Perspective](https://md.research.elamur.ai/free/en/papers/amin-2025-exploiting-task-reversibility-drs-parsing/) — Muhammad Saad Amin, Luca Anselma, Alessandro Mazzei · 2025 - [Generating Text from Uniform Meaning Representation](https://md.research.elamur.ai/free/en/papers/markle-2025-generating-text-uniform-meaning-representation/) — Emma Markle, Reihaneh Iranmanesh, Shira Wein · 2025 - [Rule-based Surface Realization of Romanian Weak Pronouns](https://md.research.elamur.ai/free/en/papers/gerstenberger-2025-rule-based-surface-realization-romanian/) — Ciprian Gerstenberger · 2025 - [Survey of Abstract Meaning Representation: Then, Now, Future](https://md.research.elamur.ai/free/en/papers/mansouri-2025-survey-abstract-meaning-representation-then/) — Behrooz Mansouri · 2025 - [The Role of PropBank Sense IDs in AMR-to-text Generation and Text-to-AMR Parsing](https://md.research.elamur.ai/free/en/papers/hoang-2025-role-propbank-sense-ids-amr/) — Thu Hoang, Mina Yang, Shira Wein · 2025 - [Assessing the Cross-linguistic Utility of Abstract Meaning Representation](https://md.research.elamur.ai/free/en/papers/wein-2024-assessing-cross-linguistic-utility-abstract/) — Shira Wein, Nathan Schneider · 2024 - [Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics](https://md.research.elamur.ai/free/en/papers/perrella-2024-beyond-correlation-interpretable-evaluation-machine/) — Stefano Perrella, Lorenzo Proietti, Pere-Lluís Huguet Cabot, et al. · 2024 - [How BERT Speaks Shakespearean English? Evaluating Historical Bias in Contextual Language Models](https://md.research.elamur.ai/free/en/papers/cuscito-2024-bert-speaks-shakespearean-english-evaluating/) — Miriam Cuscito, Alfio Ferrara, Martin Ruskov · 2024 - [LLM-Assisted Rule Based Machine Translation for Low/No-Resource Languages](https://md.research.elamur.ai/free/en/papers/coleman-2024-llm-assisted-rule-based-machine/) — Jared Coleman, Bhaskar Krishnamachari, Ruben Rosales, et al. · 2024 - [Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation](https://md.research.elamur.ai/free/en/papers/wein-2024-lost-translationese-reducing-translation-effect/) — Shira Wein, Nathan Schneider · 2024 - [Normalizing without Modernizing: Keeping Historical Wordforms of Middle French while Reducing Spelling Variants](https://md.research.elamur.ai/free/en/papers/rubino-2024-normalizing-without-modernizing-keeping-historical/) — Raphael Rubino, Johanna Gerlach, Jonathan Mutal, et al. · 2024 - [StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements](https://md.research.elamur.ai/free/en/papers/fisher-2024-styleremix-interpretable-authorship-obfuscation-distillation/) — Jillian Fisher, Skyler Hallinan, Ximing Lu, et al. · 2024 - [AMR-TST: Abstract Meaning Representation-based Text Style Transfer](https://md.research.elamur.ai/free/en/papers/shi-2023-amr-tst-abstract-meaning-representation/) — Kaize Shi, Xueyao Sun, Li He, et al. · 2023 - [Biblical translation: do different strategies produce divergent texts?](https://md.research.elamur.ai/free/en/papers/bertos-2023-biblical-translation-do-different-strategies/) — Elena Serrano Bertos, Miguel Ángel Vega Cernuda · 2023 - [Bridging Semantic Frameworks: mapping DRS onto AMR](https://md.research.elamur.ai/free/en/papers/pavlova-2023-bridging-semantic-frameworks-mapping-drs/) — Siyana Pavlova, Maxime Amblard, Bruno Guillaume · 2023 - [Mapping AMR to UMR: Resources for Adapting Existing Corpora for Cross-Lingual Compatibility](https://md.research.elamur.ai/free/en/papers/bonn-2023-mapping-amr-umr-resources-adapting/) — Julia Bonn, Skatje Myers, Jens E. L. Van Gysel, et al. · 2023 - [Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation](https://md.research.elamur.ai/free/en/papers/wein-2023-measuring-fine-grained-semantic-equivalence/) — Shira Wein, Zhuxin Wang, Nathan Schneider · 2023 **1** [2](https://md.research.elamur.ai/free/en/shelves/mtrans/page/2/) … [13](https://md.research.elamur.ai/free/en/shelves/mtrans/page/13/) [next →](https://md.research.elamur.ai/free/en/shelves/mtrans/page/2/) - [← previous shelf Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/) - [next shelf → Mathematics & statistics](https://md.research.elamur.ai/free/en/shelves/mathematics/) - [↑ back to shelves](https://md.research.elamur.ai/free/en/shelves/)