Papers › A Representation Learning Framework for Multi-Source Transfer Parsing
A Representation Learning Framework for Multi-Source Transfer Parsing
Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, Ting Liu
Cross-lingual model transfer has been a promising approach for inducing dependency parsers for low-resource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are not directly transferable across languages; 2. Target language-specific syntactic structures are difficult to be recovered. To address these two challenges, we present a novel representation learning framework for multi-source transfer parsing. Our framework allows multi-source transfer parsing using full lexical features straightforwardly. By evaluating on the Google universal dependency treebanks (v2.0), our best models yield an absolute improvement of 6.53% in averaged labeled attachment score, as compared with delexicalized multi-source transfer models. We also significantly outperform the state-of-the-art transfer system proposed most recently.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-lingual zero-shot dependency parsing | Universal Dependency Treebank | MULTI-PROJ | LAS | 69.3 | #3 of 3 | Archive leaderboard | report |
| Cross-lingual zero-shot dependency parsing | Universal Dependency Treebank | MULTI-PROJ | UAS | 76.4 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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