| Challenge: | Abstract Meaning Representation (AMR) graphs are graphs, rather than trees, because they contain reentrant nodes with multiple parents. |
| Approach: | They propose to use sequence-to-sequence models that encode AMR graphs into vector representations to generate sentences from AMRs. |
| Outcome: | The proposed model outperforms tree encoders in the AMR-to-text generation task by 24.40 points. |
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A Graph-to-Sequence Model for AMR-to-Text Generation (P18-1)
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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. |
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. |
Online Back-Parsing for AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | Abstract meaning representation (AMR) is a semantic graph representation that abstracts meaning away from a sentence. |
| Approach: | They propose a decoder that back predicts projected AMR graphs on target sentences . their results show superiority over previous state-of-the-art decoded graph Transformer . |
| Outcome: | The proposed model outperforms the state-of-the-art model on two AMR benchmarks. |
Graph Pre-training for AMR Parsing and Generation (2022.acl-long)
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| Challenge: | Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. |
| Approach: | They propose two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-tuning to improve structure awareness. |
| Outcome: | The proposed model is superior to pre-trained language models on AMR parsing and AMR-to-text generation tasks. |
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)
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| Challenge: | Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. |
| Approach: | They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model. |
| Outcome: | The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores . |
GPT-too: A Language-Model-First Approach for AMR-to-Text Generation (2020.acl-main)
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Manuel Mager, Ramón Fernandez Astudillo, Tahira Naseem, Md Arafat Sultan, Young-Suk Lee, Radu Florian, Salim Roukos
| Challenge: | Existing approaches to generating text from AMRs focus on training sequence-to-sequence or graph-tosequent models on annotated data. |
| Approach: | They propose a strong pre-trained language model with cycle consistency-based re-scoring to generate AMR text. |
| Outcome: | The proposed model outperforms existing methods on the English LDC2017T10 dataset. |
Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks (2020.acl-main)
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| Challenge: | Existing graph-to-sequence approaches use graph neural networks as encoders, but they lack the structure information needed to translate AMR into the graph-based data. |
| Approach: | They propose a graph-to-sequence task which aims to recover natural language from Abstract Meaning Representations (AMR) they adopt graph attention networks with higher-order neighborhood information to explore the edge relations in AMR graphs. |
| Outcome: | The proposed framework achieves state-of-the-art performance on English AMR benchmark datasets and is able to translate the AMR semantics into the natural language. |
Structural Information Preserving for Graph-to-Text Generation (2020.acl-main)
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| Challenge: | Existing models that mess up or drop the core structural information of input graphs are lacking in graph-to-text generation. |
| Approach: | They propose to leverage richer training signals to guide a graph-to-text generation model by focusing on autoencoding losses and back-propagating the losses to better calibrate the model. |
| Outcome: | Experiments on two benchmarks show the proposed model over a state-of-the-art model . two types of autoencoding losses are used to back-propagate the model based on multitask training . |
Enhancing AMR-to-Text Generation with Dual Graph Representations (D19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a linguistically-grounded semantic formalism that represents the meaning of a sentence as a directed graph. |
| Approach: | They propose a graph-to-sequence model that encodes different but complementary perspectives of the structural information contained in the graph. |
| Outcome: | The proposed model achieves state-of-the-art results on two AMR datasets. |
Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters (2023.eacl-main)
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| Challenge: | Recent approaches to text generation from Abstract Meaning Representation (AMR) have been based on neural-centered encoderdecoder architectures. |
| Approach: | They propose a structure-aware adapter which injects the input graph connectivity within PLMs using Graph Neural Networks. |
| Outcome: | The proposed adapter is robust to a variety of approaches and can be used to generate Graph-to-Text representations. |