| Challenge: | Existing algorithms for AM dependency parsing are slow and do not support linguistic principles. |
| Approach: | They propose an A* parser and a transition-based parsing algorithm which guarantee well-typedness and improve parse speed by up to 3 orders of magnitude. |
| Outcome: | The proposed algorithms guarantee well-typedness and improve parsing speed by up to 3 orders of magnitude while maintaining or improving accuracy. |
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AMR dependency parsing with a typed semantic algebra (P18-1)
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| Challenge: | Abstract Meaning Representations (AMRs) are graphs which describe the predicate-argument structure of a sentence. |
| Approach: | They propose a semantic parser which parses strings into tree representations of the compositional structure of an AMR graph. |
| Outcome: | The proposed parser outperforms baselines and standard neural techniques for supertagging and dependency tree parsing. |
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. |
Transition-based Semantic Dependency Parsing with Pointer Networks (2020.acl-main)
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| Challenge: | Existing dependency parsers cannot be directly applied, so they need to be adaptable to deal with the absence of singlehead and connectedness constraints. |
| Approach: | They propose a transition system that produces labelled directed acyclic graphs and performs semantic dependency parsing with Pointer Networks. |
| Outcome: | The proposed system outperforms graph-based models and outperformed existing models on a harder NLP problem. |
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 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 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. |
Simpler but More Accurate Semantic Dependency Parsing (P18-2)
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| Challenge: | Syntactic dependency parsing is the most popular method for automatically extracting low-level relationships between words in a sentence. |
| Approach: | They extend a syntactic dependency parser to train on and generate graph-structured representations that capture between-word relationships that are more closely related to the meaning of a sentence. |
| Outcome: | The proposed system beats the current state-of-the-art system by 0.6% and linguistically richer representations push the margin even higher. |
Compositional Semantic Parsing across Graphbanks (P19-1)
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| Challenge: | Existing semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. |
| Approach: | They propose a compositional neural semantic parser which achieves competitive accuracies across graphbanks. |
| Outcome: | The proposed system achieves competitive accuracies across a variety of graphbanks. |
Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited (D19-1)
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| Challenge: | In recent years, dependency parsing has shifted from discrete features to neural networks and continuous representations. |
| Approach: | They propose to use deep contextualized word embeddings to pack information about global sentence structure into local feature representations to make the two approaches virtually equivalent in terms of accuracy and error profile. |
| Outcome: | The proposed model improves the accuracy and error profile of transition-based and graph-based dependency parsers on 13 languages. |
AMR Parsing as Sequence-to-Graph Transduction (P19-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is the task of transducing natural language text into AMR, a graphbased formalism used for capturing sentence-level semantics. |
| Approach: | They propose a model that treats AMR parsing as sequence-to-graph transduction by aligner-free, and can be effectively trained with limited amounts of labeled AMR data. |
| Outcome: | The proposed model outperforms all previously reported SMATCH scores on AMR 2.0 (76.3%) and AMR 1.0 (70.2%). |