| Challenge: | Existing models for semantic parsing focus on structure-based models, but none deal with dependency information. |
| Approach: | They propose a dependency-based hybrid tree model which converts natural language utterances into machine interpretable meaning representations. |
| Outcome: | The proposed model achieves state-of-the-art performance across eight languages and is highly tractable inferenceable. |
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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. |
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. |
Context Dependent Semantic Parsing: A Survey (2020.coling-main)
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| Challenge: | Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. |
| Approach: | They propose to use contextual information to translate natural language utterances into machine-readable meaning representations. |
| Outcome: | The proposed methods do not utilize contextual information, which could boost the semantic parsing systems. |
Parsing All: Syntax and Semantics, Dependencies and Spans (2020.findings-emnlp)
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| Challenge: | Syntactic and semantic structures are key linguistic contextual clues, but few studies have explored how they can be used to improve syntactical parsing. |
| Approach: | They propose a syntactic and semantic parsing model which integrates syntaktic information in the encoder of neural network and benefits from two representation formalisms in a uniform way. |
| Outcome: | The proposed model achieves state-of-the-art or competitive results on both span and dependency representations and on Penn Treebank. |
Semantics as a Foreign Language (D18-1)
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| Challenge: | (2017): Syntactic grammars capture propositions, but graph-based representations aim to capture a wider notion of propositions. |
| Approach: | They propose a neural sequence-to-sequence framework which can recover syntactic linearizations by a sequence-based approach. |
| Outcome: | The proposed framework performs almost on-par with previous state-of-the-art approaches while requiring less parallel training annotations. |
AMR Parsing with Latent Structural Information (2020.acl-main)
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| Challenge: | Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences. |
| Approach: | They investigate parsing AMR with explicit dependency structures and interpretable latent structures. |
| Outcome: | The proposed model achieves best results on both AMR 2.0 and AMR 1.0 . the proposed model has been adopted in downstream NLP tasks, including text summarization and question answering. |
Graph-Based Decoding for Task Oriented Semantic Parsing (2021.findings-emnlp)
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| Challenge: | Existing paradigms for semantic parsing are sequence-to-sequence and AMR parsers. |
| Approach: | They propose to formulate parsing as a sequence-to-sequence task using graph-based decoding techniques developed for syntactic parsers. |
| Outcome: | The proposed approach is competitive with sequence decoders on the standard setting and offers significant improvements in data efficiency and data availability. |
Coarse-to-Fine Decoding for Neural Semantic Parsing (P18-1)
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| Challenge: | Experimental results show that semantic parsing is more efficient than using simple decoders. |
| Approach: | They propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. |
| Outcome: | The proposed architecture consistently improves performance on four datasets characteristic of different domains and meaning representations. |
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. |
Parsing into Variable-in-situ Logico-Semantic Graphs (2020.acl-main)
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| Challenge: | a new type of graph-based meaning representation allows analysis for scope-related phenomena. |
| Approach: | They propose variable-in-situ logico-semantic graphs to bridge gap between semantic graph and logical form parsing. |
| Outcome: | The proposed graph-based meaning representation achieves 92.39% accuracy in terms of elementary dependency match . the output of the proposed parser is highly coherent . |