| Challenge: | 2 is a neural model that maps a linearization of Dependency MRS to text . 1 is based on a BLEU score of 66.11 when trained on gold data . |
| Approach: | They propose to use Minimal Recursion Semantics to generate high-quality text from structured representations. |
| Outcome: | The proposed model achieves a BLEU score of 77.17 on the full test set and 83.37 on the subset of test data most closely matching the silver data domain. |
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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. |
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. |
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. |
| Approach: | They propose to use sequence-to-sequence models that encode AMR graphs into vector representations to generate sentences from AMRs. |
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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. |
A Partially Rule-Based Approach to AMR Generation (N19-3)
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| Challenge: | Abstract Meaning Representation (AMR) is a representation of a sentence as a labeled graph . because of these abstractions, it can be difficult to generate from AMR back to a fluent English sentence . |
| Approach: | They propose a new approach to generating English text from Abstract Meaning Representation (AMR) it is largely rule-based, supplemented by a language model and simple statistical linearization models . they also address difficulties of automatically evaluating AMR generation systems . |
| Outcome: | The proposed approach produces a fluent English sentence with a high quality . it is difficult to generate from an AMR back to a sentence which preserves original meaning . |
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. |
Infusing Finetuning with Semantic Dependencies (2021.tacl-1)
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| Challenge: | Several diagnostics help to localize the benefits of our approach. |
| Approach: | They apply convolutional graph encoders to integrate semantic parses into task-specific finetuning. |
| Outcome: | The proposed approach yields benefits to natural language understanding (NLU) tasks in the GLUE benchmark. |
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. |
| Approach: | They propose a neural semantic parsing approach which models semantic par- sing as an end-to-end semantic graph generation process. |
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Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation (D18-1)
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| Challenge: | Recent work on abstractive summarization has made progress with neural encoder-decoder architectures, but these models lack explicit semantic modeling of the source document and its summary. |
| Approach: | They extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which they guide using the source document. |
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Text Generation from Discourse Representation Structures (2021.naacl-main)
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| Challenge: | Existing models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs) . |
| Approach: | They propose neural models to generate text from formal meaning representations based on Discourse Representation Structures (DRSs). |
| Outcome: | The proposed model achieves competitive performance on the GMB benchmark against several strong baselines. |