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Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing

25 Feb 2019NAACL 2019 6arXiv:1902.09492archive 2025-07-28

Tal Schuster, Ori Ram, Regina Barzilay, Amir Globerson

We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. While contextual embeddings have been shown to yield richer representations of meaning compared to their static counterparts, aligning them poses a challenge due to their dynamic nature. To this end, we construct context-independent variants of the original monolingual spaces and utilize their mapping to derive an alignment for the context-dependent spaces. This mapping readily supports processing of a target language, improving transfer by context-aware embeddings. Our experimental results demonstrate the effectiveness of this approach for zero-shot and few-shot learning of dependency parsing. Specifically, our method consistently outperforms the previous state-of-the-art on 6 tested languages, yielding an improvement of 6.8 LAS points on average.

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TalSchuster/CrossLingualELMo officialmentioned in papermentioned on GitHubpytorchMIT report
TalSchuster/CrossLingualContextualEmb mentioned on GitHubpytorch report

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read_examples TalSchuster/CrossLingualELMo/gen_anchors_bert.py official repository ran · our draft was wrong MIT (permissive) · 6d45258b5c56fb25 · report
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Tasks

Cross-lingual zero-shot dependency parsingDependency ParsingFew-Shot LearningWord Embeddings

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Cross-lingual zero-shot dependency parsing Universal Dependency Treebank Cross-Lingual ELMo LAS 77.3 #1 of 3 Archive leaderboard report
Cross-lingual zero-shot dependency parsing Universal Dependency Treebank Cross-Lingual ELMo UAS 84.2 #1 of 3 Archive leaderboard report

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