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. |
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| Challenge: | et al., 2013) examines the current state-of-the-art in AMR parsing . current models violate structural constraints, but they can corrupt graphs . |
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AMR Parsing as Sequence-to-Graph Transduction (P19-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is the task of transducing natural language text into AMR, a graphbased formalism used for capturing sentence-level semantics. |
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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. |
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| Challenge: | Existing parsing systems use local or global models of the parser state to improve performance. |
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| Challenge: | Abstract Meaning Representation parsing is a sentence-to-graph prediction task . graph nodes are semantically based on one or more sentence tokens, so implicit alignments can be derived. |
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ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs (2022.findings-naacl)
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| Challenge: | Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations. |
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| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
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Dependency Parsing with Backtracking using Deep Reinforcement Learning (2022.tacl-1)
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| Challenge: | Greedy algorithms for NLP such as transition-based parsing are prone to error propagation. |
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Better Transition-Based AMR Parsing with a Refined Search Space (D18-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsers require a pipeline approach to learn concepts and relationships. |
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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. |
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