Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences
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Abstract (arXiv)
The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer. Nevertheless, little empirical work has been done on quantifying the prevalence of different syntactic divergences across language pairs. We propose a framework for extracting divergence patterns for any language pair from a parallel corpus, building on Universal Dependencies. We show that our framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation. We further present a novel dataset, a manually word-aligned subset of the Parallel UD corpus in five languages, and use it to perform a detailed corpus study. We demonstrate the usefulness of the resulting analysis by showing that it can help account for performance patterns of a cross-lingual parser.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 May 2020.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 1.
9 papers.
- 2021Meta-learning via Language Model In-context Tuningcited by 164
- 2024To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translationcited by 10
- 2022PreQuEL: Quality Estimation of Machine Translation Outputs in Advancecited by 9
- 2021Semantics-aware Attention Improves Neural Machine Translationcited by 5
- 2021On the Relation between Syntactic Divergence and Zero-Shot Performancecited by 4
- 2023Improving Cross-Lingual Transfer through Subtree-Aware Word Reorderingcited by 4
- 2021Part of Speech and Universal Dependency effects on English Arabic Machine Translationcited by 1
- 2024Locally Measuring Cross-lingual Lexical Alignment: A Domain and Word Level Perspectivecited by 1
- 2026Unsupervised Cross-Lingual Part-of-Speech Tagging with Monolingual Corpora Onlycited by 0
- 2026Unsupervised Cross-Lingual Part-of-Speech Tagging with Monolingual Corpora Only10 Feb 2026
- 2024Locally Measuring Cross-lingual Lexical Alignment: A Domain and Word Level Perspective7 Oct 2024
- 2024To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation2 Jan 2024
- 2023Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering20 Oct 2023
- 2022PreQuEL: Quality Estimation of Machine Translation Outputs in Advance18 May 2022
- 2021Meta-learning via Language Model In-context Tuning15 Oct 2021
- 2021Semantics-aware Attention Improves Neural Machine Translation13 Oct 2021
- 2021On the Relation between Syntactic Divergence and Zero-Shot Performance9 Oct 2021
- 2021Part of Speech and Universal Dependency effects on English Arabic Machine Translation1 Jun 2021