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

arXiv: https://arxiv.org/abs/2107.06055 · HTML: https://mlsanity.com/paper/2107.06055/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Theoretical Limitations of Self-Attention in Neural Sequence Models](https://mlsanity.com/paper/1906.06755/) (2019)
- [Colorless green recurrent networks dream hierarchically](https://mlsanity.com/paper/1803.11138/) (2018)
- [What do Neural Machine Translation Models Learn about Morphology?](https://mlsanity.com/paper/1704.03471/) (2017)
- [Achieving Human Parity on Automatic Chinese to English News Translation](https://mlsanity.com/paper/1803.05567/) (2018)
- [Jointly Learning to Align and Translate with Transformer Models](https://mlsanity.com/paper/1909.02074/) (2019)
- [The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English](https://mlsanity.com/paper/1902.01382/) (2019)
- [Are All Languages Equally Hard to Language-Model?](https://mlsanity.com/paper/1806.03743/) (2018)
- [What Kind of Language Is Hard to Language-Model?](https://mlsanity.com/paper/1906.04726/) (2019)
- [The Importance of Being Recurrent for Modeling Hierarchical Structure](https://mlsanity.com/paper/1803.03585/) (2018)
- [Contextual Parameter Generation for Universal Neural Machine Translation](https://mlsanity.com/paper/1808.08493/) (2018)
- [Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages](https://mlsanity.com/paper/1903.06400/) (2019)
- [Tree-to-Sequence Attentional Neural Machine Translation](https://mlsanity.com/paper/1603.06075/) (2016)
- [It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information](https://mlsanity.com/paper/2005.02354/) (2020)
- [The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages](https://mlsanity.com/paper/1710.03838/) (2017)
- [Word-order biases in deep-agent emergent communication](https://mlsanity.com/paper/1905.12330/) (2019)
- [Predicting Declension Class from Form and Meaning](https://mlsanity.com/paper/2005.00626/) (2020)
- [Inducing Grammars with and for Neural Machine Translation](https://mlsanity.com/paper/1805.10850/) (2018)

## Built on by (most cited first, 8 of 8)

- [Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off](https://mlsanity.com/paper/2301.13083/) (2023)
- [DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages](https://mlsanity.com/paper/2205.12215/) (2022)
- [NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communication](https://mlsanity.com/paper/2407.13999/) (2024)
- [Semantics-aware Attention Improves Neural Machine Translation](https://mlsanity.com/paper/2110.06920/) (2021)
- [LITERA: An LLM Based Approach to Latin-to-English Translation](https://mlsanity.com/paper/2504.10660/) (2025)
- [Assessing the Impact of Typological Features on Multilingual Machine Translation in the Age of Large Language Models](https://mlsanity.com/paper/2602.03551/) (2026)
- [Vocabulary shapes cross-lingual variation of word-order learnability in language models](https://mlsanity.com/paper/2603.19427/) (2026)
- [Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models](https://mlsanity.com/paper/2608.15129/) (2026)

## 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.