| Challenge: | Abstract Meaning Representation (AMR) parsers require a pipeline approach to learn concepts and relationships. |
| Approach: | They propose to use a transition-based search space to conduct a new compact AMR graph and an improved oracle to perform the search. |
| Outcome: | The proposed system achieves the state-of-the-art performance on various datasets with minimal additional information. |
Similar Papers
Inducing and Using Alignments for Transition-based AMR Parsing (2022.naacl-main)
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Andrew Drozdov, Jiawei Zhou, Radu Florian, Andrew McCallum, Tahira Naseem, Yoon Kim, Ramón Astudillo
| Challenge: | Abstract Meaning Representation parsers rely on node-to-word alignments, but lack the complexity of the pipeline. |
| Approach: | They propose a neural aligner for abstract meaning representation that learns node-to-word alignments without relying on pipelines. |
| Outcome: | The proposed approach improves accuracy and generalization from AMR2.0 to AMR3.0 corpora. |
An AMR Aligner Tuned by Transition-based Parser (D18-1)
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| Challenge: | Experimental results show that our AMR aligner outperforms the rule-based aligner by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
| Approach: | They propose a rich resource enhanced AMR aligner which produces multiple alignments and a new transition system for AMR parsing along with its oracle parser. |
| Outcome: | The proposed AMR aligner outperforms the current state-of-the-art parser by achieving higher alignment F1 score and consistently improving two open-source AMR parsers. |
Transition-Based Chinese AMR Parsing (N18-2)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation where the meaning of a sentence is encoded as a rooted, directed and acyclic graph. |
| Approach: | They propose a transition-based AMR parsing framework for Chinese to be used in the next generation of AMR. |
| Outcome: | The proposed parser is based on the Chinese AMR bank. |
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing (2021.emnlp-main)
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| Challenge: | Recent work shows that pre-trained sequence-to-sequence Transformer models are effective in predicting linearized Abstract Meaning Representation graphs. |
| Approach: | They propose a structure-aware transition-based approach to AMR parsing that integrates general pre-trained sequence-to-sequence language models with a structured transition set. |
| Outcome: | The proposed approach retains the desirable properties of previous approaches while reaching the new parsing state of the art for AMR 2.0. |
Pushing the Limits of AMR Parsing with Self-Learning (2020.findings-emnlp)
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Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos
| Challenge: | Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years due to the impact of transfer learning and the development of novel architectures specific to AMR. |
| Approach: | They propose to use AMR annotations to generate synthetic text and refine actions oracle without additional human annotations for AMR parsing. |
| Outcome: | The proposed models improve on AMR 1.0 and 2.0 without human annotations. |
A Survey of AMR Applications (2024.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that takes the form of a rooted, directed graph. |
| Approach: | They analyze more than 100 papers which use Abstract Meaning Representation (AMR) they highlight the range of applications for which AMR has been harnessed and techniques for incorporating it . they also highlight broader AMR engineering patterns and outline areas of future work that seem ripe for AMR incorporation. |
| Outcome: | The results highlight the range of applications for which AMR has been harnessed and the techniques for incorporating it into those applications. |
Cross-domain Generalization for AMR Parsing (2022.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) parsing aims to predict an AMR graph from textual input. |
| Approach: | They evaluate five representative AMR parsers on five domains and analyze challenges to cross-domain parsing. |
| Outcome: | The proposed method reduces the domain distribution divergence of text and AMR features on two out-of-domain sets. |
World Knowledge for Abstract Meaning Representation Parsing (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsers are based on annotated graphs, but there is still room for improvement . |
| Approach: | They examine the role played by world knowledge in parsing errors in a state-of-the-art parser . they examine the effects of different types of world knowledge on parsers . |
| Outcome: | The proposed model improves on multiple fine-grained metrics, including a 6% increase in named entity F-score, and provides insight into the potential of world knowledge for future work in Abstract Meaning Representation parsing. |
A Transition-Based Algorithm for Unrestricted AMR Parsing (N18-2)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation language to map the meaning of English sentences into directed, cycled, labeled graphs. |
| Approach: | They propose a left-to-right non-projective transition-based parser that handles cycles and reentrancy natively within the transition system itself. |
| Outcome: | The proposed algorithm handles reentrancy and arbitrary cycles natively, i.e. within the transition system itself. |
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning (P19-1)
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| Challenge: | Abstract meaning representations (AMRs) are labeled directed acyclic graphs that represent a non intersentential abstraction of natural language with broad-coverage semantic representations. |
| Approach: | They build upon a transition-based AMR parser that uses Stack-LSTMs and augment training with policy learning. |
| Outcome: | The proposed parser performs comparable to the best published parsers. |