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A Survey : Neural Networks for AMR-to-Text

arXiv 2206.07328 ↗First posted 15 Jun 2022cs.CL cs.LGcited by 0

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Abstract (arXiv)

AMR-to-text is one of the key techniques in the NLP community that aims at generating sentences from the Abstract Meaning Representation (AMR) graphs. Since AMR was proposed in 2013, the study on AMR-to-Text has become increasingly prevalent as an essential branch of structured data to text because of the unique advantages of AMR as a high-level semantic description of natural language. In this paper, we provide a brief survey of AMR-to-Text. Firstly, we introduce the current scenario of this technique and point out its difficulties. Secondly, based on the methods used in previous studies, we roughly divided them into five categories according to their respective mechanisms, i.e., Rules-based, Seq-to-Seq-based, Graph-to-Seq-based, Transformer-based, and Pre-trained Language Model (PLM)-based. In particular, we detail the neural network-based method and present the latest progress of AMR-to-Text, which refers to AMR reconstruction, Decoder optimization, etc. Furthermore, we present the benchmarks and evaluation methods of AMR-to-Text. Eventually, we provide a summary of current techniques and the outlook for future research.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 45 of its 74 references that are in the map. First posted on arXiv 15 Jun 2022.

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45 of 74 references.

References · 45

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Graph Attention Networkscited by 8,854
  5. 2016Densely Connected Convolutional Networkscited by 6,691
  6. 2017Inductive Representation Learning on Large Graphscited by 6,284
  7. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  8. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  9. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  10. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  11. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
  12. 2018Self-Attention with Relative Position Representationscited by 1,112
33 more
  1. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  2. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  3. 2015Gated Graph Sequence Neural Networkscited by 792
  4. 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 754
  5. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  6. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 666
  7. 2017Convolutional Sequence to Sequence Learningcited by 550
  8. 2020A Survey of Evaluation Metrics Used for NLG Systemscited by 164
  9. 2020Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metricscited by 155
  10. 2020Investigating Pretrained Language Models for Graph-to-Text Generationcited by 117
  11. 2016Modeling Coverage for Neural Machine Translationcited by 110
  12. 2019Do Massively Pretrained Language Models Make Better Storytellers?cited by 90
  13. 2019Graph Transformer for Graph-to-Sequence Learningcited by 82
  14. 2018Graph-to-Sequence Learning using Gated Graph Neural Networkscited by 61
  15. 2017Neural AMR: Sequence-to-Sequence Models for Parsing and Generationcited by 56
  16. 2018A Graph-to-Sequence Model for AMR-to-Text Generationcited by 56
  17. 2021Structural Adapters in Pretrained Language Models for AMR-to-text Generationcited by 48
  18. 2018Toward Abstractive Summarization Using Semantic Representationscited by 43
  19. 2020GPT-too: A language-model-first approach for AMR-to-text generationcited by 38
  20. 2019Modeling Graph Structure in Transformer for Better AMR-to-Text Generationcited by 35
  21. 2019Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learningcited by 29
  22. 2020Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMRcited by 25
  23. 2019Enhancing AMR-to-Text Generation with Dual Graph Representationscited by 24
  24. 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
  25. 2019Structural Neural Encoders for AMR-to-text Generationcited by 19
  26. 2020A Human Evaluation of AMR-to-English Generation Systemscited by 13
  27. 2020Multilingual AMR-to-Text Generationcited by 12
  28. 2020Online Back-Parsing for AMR-to-Text Generationcited by 9
  29. 2020Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generationcited by 8
  30. 2015Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Textcited by 5
  31. 2017AMR-to-text Generation with Synchronous Node Replacement Grammarcited by 5
  32. 2016AMR-to-text generation as a Traveling Salesman Problemcited by 4
  33. 2018Factorising AMR generation through syntaxcited by 4

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