Add zero-shot cross-lingual d-parsing results (sebastianruder#621)

* Add zero-shot cross-lingual d-parsing results Add results from Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing. * Update dependency_parsing.md
honghanhh · Aug 30, 2022 · 8ad2a58 · 8ad2a58
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@@ -79,10 +79,13 @@ gold POS-tags.
 
 | Model                                      |  UAS  |  LAS  | Paper / Source                                                                                                                               | Code                                                          |
 | ------------------------------------------ | :---: | :---: | -------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------- |
+| XLM-R + SubDP (Shi et al., 2022) | --- | 79.6* | [Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing](https://aclanthology.org/2022.acl-long.452/) | [Official](https://aclanthology.org/attachments/2022.acl-long.452.software.zip)
 | Cross-Lingual ELMo (Schuster et al., 2019) | 84.2  | 77.3  | [Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing](https://arxiv.org/abs/1902.09492) | [Official](https://github.com/TalSchuster/CrossLingualELMo)   |
 | MALOPA (Ammar et al., 2016)                |       | 70.5  | [Many Languages, One Parser](https://www.transacl.org/ojs/index.php/tacl/article/view/892)                                                   | [Official](https://github.com/clab/language-universal-parser) |
 | Guo et al. (2016)                          | 76.7  | 69.9  | [A representation learning framework for multi-source transfer parsing](https://dl.acm.org/citation.cfm?id=3016100.3016284)                  |
 
+*: Evaluated on four target languages.
+
 # Unsupervised dependency parsing
 
 Unsupervised dependency parsing is the task of inferring the dependency parse of sentences without any labeled training data.