| 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 . |
| Outcome: | The proposed metric reveals the kind of dependencies that require high effort during training . it also shows that cross-lingual parsers can provide better quality information . |
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Some Languages Seem Easier to Parse Because Their Treebanks Leak (2020.emnlp-main)
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| Challenge: | Cross-language differences in (universal) dependency parsing performance are mostly attributed to treebank size, average sentence length, average dependency length, morphological complexity, and domain differences. |
| Approach: | They compute graph isomorphisms and find that treebank size is a factor that influences parsing performance. |
| Outcome: | The results show that the overlap between training and test graphs explain more of the observed variation than standard explanations such as the above. |
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
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| Challenge: | Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans. |
| Approach: | They propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters and then verify their quality using automatic and human evaluations. |
| Outcome: | The proposed method improves the robustness of English parsing models by relying on adversarial training and model ensembling. |
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. |
| Approach: | They propose a method to make the most of heterogeneous treebanks when training a monolingual parser. |
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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. |
| Approach: | They propose to survey existing approaches to unsupervised dependency parsing . they identify two major classes of approaches and discuss recent trends . |
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Revisiting the Effects of Leakage on Dependency Parsing (2022.findings-acl)
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| Challenge: | Recent work shows that treebank size and linguistic variation are important factors that explain the variation in dependency parsing performance. |
| Approach: | They propose a measure of leakage that explains and correlates with observed performance variation. |
| Outcome: | The proposed measure explains and correlates with observed performance variation. |
A Root of a Problem: Optimizing Single-Root Dependency Parsing (2021.emnlp-main)
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| Challenge: | Graph-based dependency parsers can be improved without compromising on accuracy or accuracy. |
| 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 . |
| Outcome: | The proposed approach finds the optimal dependency tree without loss of accuracy or optimality. |
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. |
| Approach: | They propose to use dependency trees as sequence labels to obtain fast and accurate parsers using a conventional BILSTM-based model. |
| Outcome: | The proposed models are conceptually simple, not needing traditional parsing algorithms or auxiliary structures, and provide a good speed-accuracy tradeoff, with results competitive with more complex approaches. |
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages (D19-1)
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| Challenge: | Large annotated treebanks are available for only a tiny fraction of the world's languages, and there is a wealth of literature on strategies for parsing with few resources. |
| Approach: | They propose three strategies for improving low-resource parsers: data augmentation, cross-lingual training, and transliteration. |
| Outcome: | The proposed methods improve low-resource parsers by using data augmentation, cross-lingual training, and transliteration. |
Bayesian Learning for Neural Dependency Parsing (N19-1)
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| Challenge: | Several approaches for dependency parsing in the small data regime have been proposed. |
| Approach: | They propose to use stochastic gradient Langevin dynamics to generate samples from the approximated posterior to overcome the computational and statistical costs of the approximate inference step. |
| Outcome: | The proposed model outperforms the biaffine model on 6 languages with less than 5k training instances and improves across five languages. |
Revisiting Tri-training of Dependency Parsers (2021.emnlp-main)
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| Challenge: | Pre-trained word embeddings and self-training have been used in dependency parsing tasks for years. |
| Approach: | They compare tri-training and pretrained word embeddings in dependency parsing . they use language-specific FastText and ELMo embedds and multilingual BERT embedders . |
| Outcome: | The proposed methods are tri-training and pretrained word embeddings. |