Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations (2020.coling-main)
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| Challenge: | Existing dependency parsing models for Arabic use complementary annotations, CATiB and UD treebanks, and partially created trees for one annotation are also available to the other as features for the score function. |
| Approach: | They propose to use Arabic dependency annotations to parse projective dependency trees using CATiB and UD treebanks. |
| Outcome: | The proposed model gives 9.9% error reduction on CATiB and 6.1% on UD compared to a strong baseline and ablation tests show that the main contribution is given by sharing tree representation between tasks, and not simply sharing biLSTM layers as is often performed in NLP multitask systems. |
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| Challenge: | Existing methods for developing broad-coverage semantic dependency parsers for languages without semantically annotated data are limited to English, Czech and Chinese. |
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High-order Joint Constituency and Dependency Parsing (2024.lrec-main)
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| Challenge: | Syntactic parsing aims to reveal how sentences are syntactically structured. |
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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. |
| Approach: | They propose to use dependency trees as sequence labels to obtain fast and accurate parsers using a conventional BILSTM-based model. |
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Development of a Multilingual CCG Treebank via Universal Dependencies Conversion (2022.lrec-1)
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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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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. |
Sequence Labeling Parsing by Learning across Representations (P19-1)
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| Challenge: | Constituency and dependency parsing are the main abstractions for representing syntactic structure of sentences . constituency parsers are considered disjointed tasks, and their improvements have been obtained separately. |
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Combining (Second-Order) Graph-Based and Headed-Span-Based Projective Dependency Parsing (2022.findings-acl)
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| Challenge: | Existing graph-based methods that score dependency trees do not score dependency arcs at all. |
| Approach: | They propose a headed-span-based method that decomposes the score of a dependency tree into scores of headed spans. |
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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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Efficient Second-Order TreeCRF for Neural Dependency Parsing (2020.acl-main)
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| Challenge: | In the deep learning (DL) era, dependency parsing models are extremely simplified with little hurt on performance thanks to the remarkable capability of multi-layer BiLSTMs in context representation. |
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