Papers by Ziyi Shou
Incorporating EDS Graph for AMR Parsing (2021.starsem-1)
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| Challenge: | AMR is abstract and conceptual, while EDS is low level, closer to the lexical structures of the given sentences. |
| Approach: | They propose to add EDS graphs as additional semantic features to AMR parsers by adding transition-based parser to add LSTM layer and GCN layer. |
| Outcome: | The proposed parser adds EDS graphs as additional semantic features to boost performance . Currently the parsing accuracies for AMR are in low 80s, while they can be improved by adding more information from EDS. |
Evaluate AMR Graph Similarity via Self-supervised Learning (2023.acl-long)
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| Challenge: | Current AMR metrics do not consider the entire structure of AMR graphs . |
| Approach: | They propose to learn automatic AMR graph similarity evaluation metric by encoding AMR to a pre-trained language model and using GNN adapters to capture structural information of AMR diagrams. |
| Outcome: | The proposed metric significantly improves the correlations with human semantic scores and remains robust under diverse challenges. |
AMR-DA: Data Augmentation by Abstract Meaning Representation (2022.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation for NLP/NLU. |
| Approach: | They propose to use AMR-DA for data augmentation in NLP . they use sentence-level techniques like back translation and token-level methods like EDA . |
| Outcome: | The proposed method outperforms EDA and AEDA and improves on STS and text classification tasks. |