| Challenge: | Existing approaches to graph-to-sequence learning ignore the full graph structure, discarding key information. |
| Approach: | They propose a graph-to-sequence learning model that encodes the full graph structure and an input transformation that allows nodes and edges to have their own hidden representations. |
| Outcome: | The proposed model outperforms baselines in generation from AMR graphs and syntax-based neural machine translation while retaining the full graph structure. |
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism that encodes the meaning of a sentence as a rooted, directed graph. |
| Approach: | They propose a neural graph-to-sequence model that leverages LSTM to encode a linearized AMR structure. |
| Outcome: | The proposed model outperforms existing methods on a benchmark. |
Deep Learning on Graphs for Natural Language Processing (2021.naacl-tutorials)
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| Challenge: | Graph Neural Networks (GNNs) are powerful tools for non-Euclidean data modeling and are used in many graph-related NLP tasks. |
| Approach: | This tutorial will cover applying deep learning on graph techniques to NLP using Graph Neural Networks (GNNs) Graph4NLP is the first library for researchers and practitioners for easy use of GNNs for various NLP tasks. |
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Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
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| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
| Approach: | They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding. |
| Outcome: | The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0. |
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem (2020.findings-emnlp)
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| Challenge: | Graph2Tree model encodes graph-structured input and decodes tree-structures output. |
| Approach: | They propose a novel Graph-to-Tree Neural Network consisting of a graph encoder and a hierarchical tree decoder that encodes an augmented graph-structured input and decodes a tree-structure-output. |
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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. |
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Heterogeneous Graph Transformer for Graph-to-Sequence Learning (2020.acl-main)
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| Challenge: | Recent studies ignore the indirect relations between distance nodes, or treat indirect relations and direct relations in the same way. |
| Approach: | They propose a graph-to-sequence (Graph2Seq) encoder which models graph structure to model different relations in individual subgraphs of the original graph. |
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Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
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Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing (P18-1)
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| Challenge: | Existing methods for semantic parsing are difficult to design and learn, especially in wideopen domains. |
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Structural Neural Encoders for AMR-to-text Generation (N19-1)
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| Challenge: | Abstract Meaning Representation (AMR) graphs are graphs, rather than trees, because they contain reentrant nodes with multiple parents. |
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AMR-To-Text Generation with Graph Transformer (2020.tacl-1)
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| Challenge: | Abstract meaning representation (AMR)-to-text generation is challenging task for natural language processing. |
| Approach: | They propose a graph-to-sequence model that directly encodes AMR graphs and learns node representations. |
| Outcome: | The proposed model outperforms the current state-of-the-art neural approach by 1.5 BLEU points on LDC2015E86 and 4.8 BLUE points on the LDC2017T10 and achieves new state- of-the art performance. |