| Challenge: | Dependency parsing research focuses on improving accuracy of single-tree predictions . ambiguity is inherent to natural language syntax, and communicating it is important for error analysis . |
| Approach: | They propose a transition sampling algorithm to sample from the full joint distribution of parse trees defined by a model and demonstrate its usefulness. |
| Outcome: | The proposed method can be used to propagate parse uncertainty to two downstream applications. |
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| Challenge: | Existing bubble representations encoding coordination boundaries and internal relationships are difficult to detect and parse . |
| Approach: | They propose a bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. |
| Outcome: | The proposed bubble parser beats state-of-the-art approaches on coordination structure prediction . the proposed system is based on a GENIA corpus and a Penn treebank . |
Valency-Augmented Dependency Parsing (D18-1)
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| 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. |
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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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Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers (N18-2)
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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. |
| Approach: | They generalize Cohen et al.'s (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference. |
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Global Transition-based Non-projective Dependency Parsing (P18-1)
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| Challenge: | Until recently, transition-based dependency parsers were limited to approximate inference due to their incompatibility with rich feature models. |
| Approach: | They propose a transition-based parser with high coverage on non-projective treebanks to support non- projective parsing. |
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A Unifying Theory of Transition-based and Sequence Labeling Parsing (2020.coling-main)
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| Challenge: | Existing parsers that read sentences from left to right are not learning to parse them. |
| Approach: | They propose a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. |
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Syntax in End-to-End Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | tutorial focuses on syntactic parsing and syntax in end-to-end natural language processing (NLP) tasks. |
| Approach: | tutorial will introduce syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks. |
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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 . |
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Syntactic Nuclei in Dependency Parsing – A Multilingual Exploration (2021.eacl-main)
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| Challenge: | Existing models for syntactic dependency parsing assume words are elementary units that enter into dependency relations. |
| Approach: | They propose to use composition functions to make a transition-based dependency parser aware of the notion of nucleus. |
| Outcome: | The proposed concept of nucleus gives small but significant improvements in parsing accuracy on 12 languages. |
Unbiased and Efficient Sampling of Dependency Trees (2022.emnlp-main)
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| Challenge: | linguistic constraints in dependency trees are not part of the definition of spanning trees. |
| Approach: | They propose to use a constraint that requires a single root to be incorporated into dependency tree sampling . they propose to reduce the asymptotic runtime of sampling k trees without replacement to O(kn3) |
| Outcome: | The proposed algorithms are asymptotically and practically more efficient . they reduce the runtime of the fastest algorithm for sampling with replacement to O(kn3) |