Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
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Abstract (arXiv)
Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR. However, this assumes a high quality of generated AMRs, potentially limiting the transferability to the target task. In this paper, we investigate different techniques for automatically generating AMR annotations, where we aim to study which source of information yields better multilingual results. Our models trained on gold AMR with silver (machine translated) sentences outperform approaches which leverage generated silver AMR. We find that combining both complementary sources of information further improves multilingual AMR-to-text generation. Our models surpass the previous state of the art for German, Italian, Spanish, and Chinese by a large margin.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 46 references that are in the map. First posted on arXiv 8 Sep 2021.
Builds on
23 of 46 references.
References · 23
- 2017Attention Is All You Needcited by 59,932
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
- 2018Adafactor: Adaptive Learning Rates with Sublinear Memory Costcited by 896
- 2020Investigating Pretrained Language Models for Graph-to-Text Generationcited by 117
- 2019Graph Transformer for Graph-to-Sequence Learningcited by 82
- 2018Graph-to-Sequence Learning using Gated Graph Neural Networkscited by 61
- 2017Neural AMR: Sequence-to-Sequence Models for Parsing and Generationcited by 56
- 2018A Graph-to-Sequence Model for AMR-to-Text Generationcited by 56
- 2021Structural Adapters in Pretrained Language Models for AMR-to-text Generationcited by 48
11 more
- 2020AMR Parsing via Graph-Sequence Iterative Inferencecited by 47
- 2020GPT-too: A language-model-first approach for AMR-to-text generationcited by 38
- 2020Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelitycited by 35
- 2020Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphscited by 30
- 2019Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learningcited by 29
- 2018Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representationcited by 24
- 2019Enhancing AMR-to-Text Generation with Dual Graph Representationscited by 24
- 2017Cross-lingual Abstract Meaning Representation Parsingcited by 19
- 2020Multilingual AMR-to-Text Generationcited by 12
- 2021Bootstrapping Multilingual AMR with Contextual Word Alignmentscited by 8
- 2019Practical Semantic Parsing for Spoken Language Understandingcited by 7
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3 papers.