| Challenge: | valency analysis is a complex task that requires a large number of subcategorizations, such as the number and types of syntactic dependents. |
| Approach: | They propose a parsing approach that explicitly models the number and types of syntactic dependents as valency patterns and a probabilistic model for tagging them. |
| Outcome: | The proposed approach outperforms the state-of-the-art labeled attachment score on 53 treebanks representing 41 languages and outperformed the previous state- of-the art labeles by 0.7. |
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| Approach: | They propose two approaches to single-root dependency parsing that yield speed ups . they show that one approach is fully correct and finds the optimal dependency tree . |
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Viable Dependency Parsing as Sequence Labeling (N19-1)
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| Challenge: | Existing work on dependency parsing by sequence labeling suggested that it was impractical. |
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Parsing as Tagging (2020.lrec-1)
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| Challenge: | Existing methods for dependency parsing treat parse as tagging, but they are not perfect. |
| Approach: | They propose a simple yet accurate method that treats parsing as tagging . they use a sequence model with a bidirectional LSTM over BERT embeddings . |
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Please Mind the Root: Decoding Arborescences for Dependency Parsing (2020.emnlp-main)
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| Challenge: | a dependency tree has a root constraint, but only one edge may emanate from the root node. |
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A Survey of Unsupervised Dependency Parsing (2020.coling-main)
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| Challenge: | Syntactic dependency parsing is an important task in natural language processing . unsupervised learning of dependency parses requires training sentences to be manually annotated with their correct parse trees. |
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| Challenge: | Sequence labeling (SL) is a simple yet effective paradigm for a wide range of natural language problems. |
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Quantifying training challenges of dependency parsers (C18-1)
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| Challenge: | a new metric is introduced to evaluate the difficulty to learn a given class of dependencies . a series of systematic computations using that metric have revealed interesting properties of the 3 considered parsing algorithms . |
| Approach: | They introduce a new metric to evaluate the difficulty to learn a given class of dependencies . they use it to characterize the information conveyed by cross-lingual parsers . |
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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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Out-of-Domain Evaluation of Finnish Dependency Parsing (2022.lrec-1)
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| Challenge: | prevailing practice in academia evaluates model performance on in-domain evaluation data . however, in many real world applications data on which model is applied may differ from training data - a problem that is not addressed by current literature. |
| Approach: | They propose to use Finnish-OOD out-of-domain treebank for out- of-domain evaluation . they propose to include sections more challenging for the general parser . |
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Parsing All: Syntax and Semantics, Dependencies and Spans (2020.findings-emnlp)
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| Challenge: | Syntactic and semantic structures are key linguistic contextual clues, but few studies have explored how they can be used to improve syntactical parsing. |
| Approach: | They propose a syntactic and semantic parsing model which integrates syntaktic information in the encoder of neural network and benefits from two representation formalisms in a uniform way. |
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