| 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. |
| Approach: | They propose to extend the biaffine parser to a second-order TreeCRF extension to reduce the complexity of the inside-outside algorithm. |
| Outcome: | The proposed extension can be used to batchify the inside and Viterbi algorithms and avoid the complex outside algorithm via efficient back-propagation. |
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Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training (2020.aacl-main)
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| Challenge: | Existing approaches to dependency parsing use exact and approximate inference to find the best parse tree. |
| Approach: | They propose a second-order graph-based neural dependency parsing approach using message passing and end-to-end neural networks. |
| Outcome: | The proposed methods match the state-of-the-art second-order graph-based neural dependency parsers and have significantly faster speed in training and testing. |
Second-Order Unsupervised Neural Dependency Parsing (2020.coling-main)
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| Challenge: | supervised dependency parsers can reach a very high accuracy, but they require treebanks for training. |
| Approach: | They propose a second-order extension of unsupervised neural dependency models that incorporate grandparent-child or sibling information. |
| Outcome: | The proposed model achieves 10% improvement over the previous state-of-the-art model on the full WSJ dataset. |
Semantic Dependency Parsing with Edge GNNs (2022.findings-emnlp)
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| Challenge: | Existing semantic dependency parsers use factor graphs to generate a tree structure, but they are ill-suited for a more complex semantic relationship representation. |
| Approach: | They propose a second-order neural CRF parser that uses factor graphs to generate a dependency edge and define neighbors in terms of sibling, co-parent, and grandparent relationships. |
| Outcome: | The proposed model outperforms the first-order biaffine parser on English datasets and shows that it is more efficient than the first order. |
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks (P19-1)
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| Challenge: | Existing approaches to semantic dependency parsing are graph-based and transition-based. |
| Approach: | They propose a second-order semantic dependency parser which takes relationships between two or more edges into account. |
| Outcome: | The proposed algorithm outperforms existing approaches to parsing on graph-based approaches. |
Graph-based Dependency Parsing with Graph Neural Networks (P19-1)
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| Challenge: | In graph-based dependency parsers, learning representations is gaining in importance, and we use graph neural networks to learn the representations. |
| Approach: | They propose to use graph neural networks to learn dependency tree nodes and propose to add a new aggregation function to the system. |
| Outcome: | The proposed model achieves the best UAS and LAS on PTB (96.0%, 94.3%) without using external resources. |
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. |
| Outcome: | The proposed method improves over first-order graph-based methods, but does not score dependency arcs at all. |
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. |
Revisiting Higher-Order Dependency Parsers (2020.acl-main)
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| Challenge: | Neural encoders have allowed dependency parsers to shift from higher-order structured models to simpler first-order ones, making decoding faster and still achieving better accuracy than non-neural parser. |
| Approach: | They found that neural parsers may benefit from higher-order features when employing a powerful pre-trained encoder, such as BERT. |
| Outcome: | Using a pre-trained encoder, we found that higher-order models are more accurate on full sentence parses and match of modifier lists. |
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. |
Multi-Layer Pseudo-Siamese Biaffine Model for Dependency Parsing (2022.coling-1)
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| Challenge: | Existing work only uses biaffine method at the end of the dependency parser as a scorer, and its application in multi-layer form is ignored. |
| Approach: | They propose a multi-layer pseudo-Siamese biaffine model for neural dependency parsing that uses biaffin method as a scorer and a biaffin module to construct arc weight matrix. |
| Outcome: | The proposed model achieves state-of-the-art on PTB, CTB, and UD datasets with low efficiency loss. |