mlsanityData to 7 Oct 2026

On the Difficulty of Translating Free-Order Case-Marking Languages

arXiv 2107.06055 ↗First posted 13 Jul 2021cs.CLcited by 8

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Abstract (arXiv)

Identifying factors that make certain languages harder to model than others is essential to reach language equality in future Natural Language Processing technologies. Free-order case-marking languages, such as Russian, Latin or Tamil, have proved more challenging than fixed-order languages for the tasks of syntactic parsing and subject-verb agreement prediction. In this work, we investigate whether this class of languages is also more difficult to translate by state-of-the-art Neural Machine Translation models (NMT). Using a variety of synthetic languages and a newly introduced translation challenge set, we find that word order flexibility in the source language only leads to a very small loss of NMT quality, even though the core verb arguments become impossible to disambiguate in sentences without semantic cues. The latter issue is indeed solved by the addition of case marking. However, in medium- and low-resource settings, the overall NMT quality of fixed-order languages remains unmatched.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 24 of its 63 references that are in the map. First posted on arXiv 13 Jul 2021.

Builds on

24 of 63 references.

References · 24

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12 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 3.

8 papers.

  1. 2023Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-offcited by 12
  2. 2022DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languagescited by 11
  3. 2024NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communicationcited by 8
  4. 2021Semantics-aware Attention Improves Neural Machine Translationcited by 5
  5. 2025LITERA: An LLM Based Approach to Latin-to-English Translationcited by 3
  6. 2026Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Modelscited by 1
  7. 2026Vocabulary shapes cross-lingual variation of word-order learnability in language modelscited by 0
  8. 2026Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Modelscited by 0