mlsanityData to 14 Oct 2026

The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages

arXiv 1710.03838 ↗First posted 10 Oct 2017cs.CLcited by 19

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Abstract (arXiv)

We release Galactic Dependencies 1.0---a large set of synthetic languages not found on Earth, but annotated in Universal Dependencies format. This new resource aims to provide training and development data for NLP methods that aim to adapt to unfamiliar languages. Each synthetic treebank is produced from a real treebank by stochastically permuting the dependents of nouns and/or verbs to match the word order of other real languages. We discuss the usefulness, realism, parsability, perplexity, and diversity of the synthetic languages. As a simple demonstration of the use of Galactic Dependencies, we consider single-source transfer, which attempts to parse a real target language using a parser trained on a "nearby" source language. We find that including synthetic source languages somewhat increases the diversity of the source pool, which significantly improves results for most target languages.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Oct 2017.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 4; 2024: 3; 2025: 1; 2026: 2.

19 papers.

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  1. 2021When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfercited by 42
  2. 2023Modeling rapid language learning by distilling Bayesian priors into artificial neural networkscited by 42
  3. 2021Examining the Inductive Bias of Neural Language Models with Artificial Languagescited by 40
  4. 2025Can Language Models Learn Typologically Implausible Languages?cited by 23
  5. 2024Emergent Word Order Universals from Cognitively-Motivated Language Modelscited by 11
  6. 2023Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distributioncited by 10
  7. 2022Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studiescited by 9
  8. 2021On the Difficulty of Translating Free-Order Case-Marking Languagescited by 8
  9. 2024NeLLCom-X: A Comprehensive Neural-Agent Framework to Simulate Language Learning and Group Communicationcited by 8
  10. 2021How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?cited by 7
  11. 2022ALIGN-MLM: Word Embedding Alignment is Crucial for Multilingual Pre-trainingcited by 6
  12. 2021The Impact of Positional Encodings on Multilingual Compressioncited by 5
7 more
  1. 2023Multilingual Nonce Dependency Treebanks: Understanding how Language Models represent and process syntactic structurecited by 5
  2. 2022Word Order Matters when you Increase Maskingcited by 4
  3. 2023Improving Cross-Lingual Transfer through Subtree-Aware Word Reorderingcited by 4
  4. 2024Exploring the Robustness of Task-oriented Dialogue Systems for Colloquial German Varietiescited by 4
  5. 2026Retrievit: In-context Retrieval Capabilities of Transformers, State Space Models, and Hybrid Architecturescited by 1
  6. 2022Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?cited by 0
  7. 2026Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languagescited by 0