| Challenge: | Existing methods for dependency parsing use word order differences between source and target languages. |
| Approach: | They propose a cross-lingual transfer method that takes into account word order differences between source and target languages. |
| Outcome: | The proposed method improves on 68 treebanks (38 languages) on a target language. |
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Improving Cross-lingual Transfer through Subtree-aware Word Reordering (2023.findings-emnlp)
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| Challenge: | Recent studies show that multilingual language models are not effective when dealing with less-represented languages. |
| Approach: | They propose a powerful reordering method that learns word-order patterns conditioned on the syntactic context from a small amount of annotated data. |
| Outcome: | The proposed method outperforms baselines on a variety of tasks and is effective in both zero-shot and few-shot scenarios. |
Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank (D19-1)
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| Challenge: | Treebank translation is a promising method for cross-lingual transfer of syntactic dependency knowledge. |
| Approach: | They propose to map dependency arcs from source treebank to target translation according to word alignments. |
| Outcome: | Experiments on university dependency treebanks show that translated treebank translations are more effective than translated treebans. |
Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios (2020.emnlp-main)
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| Challenge: | a limited set of translations into one or more high-resource languages are available for POS tagging . a bi-LSTM architecture that uses contextualized word embeddings improves performance . |
| Approach: | They propose an unsupervised cross-lingual transfer approach for part-of-speech tagging . they use the Bible as parallel data to learn POS taggers for target languages . |
| Outcome: | The proposed approach improves accuracy on 12 diverse languages . the Bible is used as a parallel corpus for the study . |
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering (2020.findings-emnlp)
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| Challenge: | Existing approaches to cross-lingual dependency parsing rely on large corpus size and cost. |
| Approach: | They propose a cross-lingual dependency parsing approach based on word reordering . they propose to train a model that transfers knowledge learned in one or multiple languages to target languages . |
| Outcome: | The proposed approach outperforms the baseline approach in Hindi and Latin by 15.3% and 6.7%. |
Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections (P19-1)
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| Challenge: | Current systems for syntactic analysis tasks rely heavily on large scale annotated data. |
| Approach: | They propose to learn a generative model with a structured prior that uses labeled source and unlabeled target data jointly. |
| Outcome: | The proposed model improves on part-of-speech tagging and dependency parsing tasks on English as the only source corpus and on a wide range of target languages. |
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages (D19-1)
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| Challenge: | Large annotated treebanks are available for only a tiny fraction of the world's languages, and there is a wealth of literature on strategies for parsing with few resources. |
| Approach: | They propose three strategies for improving low-resource parsers: data augmentation, cross-lingual training, and transliteration. |
| Outcome: | The proposed methods improve low-resource parsers by using data augmentation, cross-lingual training, and transliteration. |
Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer (2026.acl-srw)
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| Challenge: | Large language models (LLMs) have advanced natural language processing, yet their benefits remain concentrated in English and a small number of high-resource languages. |
| Approach: | They fine-tuned large language models (4B–671B parameters) on Arabic and evaluated zero-shot reading comprehension on Semitic languages and non-Semitic controls. |
| Outcome: | The results show that models with weak baselines improve across all languages, whereas strong-baseline models show only marginal gains regardless of language family. |
Cheating a Parser to Death: Data-driven Cross-Treebank Annotation Transfer (L18-1)
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| Challenge: | Using annotated corpus for linguistic purposes is no longer justified . hand-crafted syntactic resources such as grammars and lexicons can be used as sources of features to guide data driven systems. |
| Approach: | They propose an efficient method for transferring annotations between two different treebanks of the same language. |
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Producing a Parallel Universal Dependencies Treebank of Ancient Hebrew and Ancient Greek via Cross-Lingual Projection (2024.lrec-main)
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| Challenge: | Using parallel treebanks, syntactic changes can be identified and evaluated in translations, redactions, and commentaries. |
| Approach: | They propose to construct a treebank of Ancient Greek containing portions of the Septuagint by word-aligning and projecting from the parallel Ancient Hebrew text. |
| Outcome: | The proposed treebank contains portions of the Hebrew Scriptures, which are translated into Ancient Greek, and is based on the results of a collaborative effort to create a crosslinguistically consistent treebank annotation scheme. |
Handling Syntactic Divergence in Low-resource Machine Translation (D19-1)
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| Challenge: | Existing approaches to neural machine translation (NMT) are dependent on limited parallel data, and can be difficult to use for many language pairs. |
| Approach: | They propose a method where target-language sentences are re-ordered to match the order of the source and used as an additional source of training-time supervision. |
| Outcome: | The proposed method improves on simulated low-resource Japanese-to-English and real low-demand Uyghur-to English scenarios. |