Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees (N18-2)
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| Challenge: | Several strategies have been proposed to overcome the projectivity constraint by introducing transition-based dependency parsers that can build non-projective dependencies. |
| Approach: | They propose a modification of dynamic oracles to allow use of non-projective data . their method consistently outperforms traditional projectivization and pseudo-projectivisation approaches . |
| Outcome: | The proposed method outperforms projectivization and pseudo-projectivisation methods on 73 treebanks and achieves significant gains for non-projective languages. |
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| Challenge: | Existing dynamic oracles for greedy parsers can handle non-projective syntax, but none are available for these types of training. |
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| Challenge: | Non-projective dependency trees account for 12.59% of all training sentences in the annotated Universal Dependencies (UD) 2.1 data. |
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4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees (2023.emnlp-main)
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| Challenge: | encodings that map trees into sequences of one discrete label per word have been proposed for constituency parsing and dependency parse. |
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| Challenge: | Existing methods for dependency parsing based on headed spans are available. |
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Parser Training with Heterogeneous Treebanks (P18-2)
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| Challenge: | In the 2017 CoNLL Shared Task on Universal Dependency Parsing, 25 languages have more than one treebank . many teams did not take advantage of the multiple treebanks, however, and trained one model per treebank instead of one model for each language. |
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