| Challenge: | Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, labeled graph. |
| Approach: | They propose a model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph. |
| Outcome: | The proposed model outperforms existing models by large margins on both input sequence and output graph. |
Similar Papers
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. |
| Approach: | They propose a model that treats AMR parsing as sequence-to-graph transduction by aligner-free, and can be effectively trained with limited amounts of labeled AMR data. |
| Outcome: | The proposed model outperforms all previously reported SMATCH scores on AMR 2.0 (76.3%) and AMR 1.0 (70.2%). |
Leveraging AMR Graph Structure for Better Sequence-to-Sequence AMR Parsing (2024.lrec-main)
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| Challenge: | Recent studies on AMR parsing often regard this task as a seq2seq translation problem. |
| Approach: | They propose to translate AMR graphs into AMR token sequences in pre-processing and recover AMR from sequences after decoding. |
| Outcome: | The proposed approach outperforms baseline and achieves 85.5 0.1 and 84.2 0.2 Smatch scores on AMR 2.0 and AMR 3.0. |
A Graph-to-Sequence Model for AMR-to-Text Generation (P18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism that encodes the meaning of a sentence as a rooted, directed graph. |
| Approach: | They propose a neural graph-to-sequence model that leverages LSTM to encode a linearized AMR structure. |
| Outcome: | The proposed model outperforms existing methods on a benchmark. |
Sequence-to-sequence AMR Parsing with Ancestor Information (2022.acl-short)
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| Challenge: | Abstract Meaning Representation (AMR) is a graph that encodes the semantic meaning of a sentence. |
| Approach: | They propose several strategies to add important ancestor information into a Transformer Decoder. |
| Outcome: | The proposed methods improve performance for both AMR 2.0 and AMR 3.0 datasets and achieve new state-of-the-art results. |
DocAMR: Multi-Sentence AMR Representation and Evaluation (2022.naacl-main)
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Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) graphs are compared to gold graphs by the Smatch metric, but lack a well-defined representation and evaluation. |
| Approach: | They propose an algorithm for deriving a unified graph representation using a super-sentential annotation method. |
| Outcome: | The proposed algorithm avoids the pitfalls of over-merging and lacks coherence from under merging. |
Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)
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| Challenge: | Abstract meaning representation (AMR) parsing is limited by the size of curated datasets. |
| Approach: | They propose a seq2seq pre-training approach to build pre-trained models on three relevant tasks. |
| Outcome: | The proposed model improves performance on three relevant tasks while maintaining the response of pre-trained models. |
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)
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| Challenge: | Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information. |
| Approach: | They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form . |
| Outcome: | The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880. |
Online Back-Parsing for AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | Abstract meaning representation (AMR) is a semantic graph representation that abstracts meaning away from a sentence. |
| Approach: | They propose a decoder that back predicts projected AMR graphs on target sentences . their results show superiority over previous state-of-the-art decoded graph Transformer . |
| Outcome: | The proposed model outperforms the state-of-the-art model on two AMR benchmarks. |
AMR Parsing with Latent Structural Information (2020.acl-main)
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| Challenge: | Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences. |
| Approach: | They investigate parsing AMR with explicit dependency structures and interpretable latent structures. |
| Outcome: | The proposed model achieves best results on both AMR 2.0 and AMR 1.0 . the proposed model has been adopted in downstream NLP tasks, including text summarization and question answering. |
Learned Incremental Representations for Parsing (2022.acl-long)
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| Challenge: | a new syntactic representation that commits to syntakic choices is proposed for humans . we use a system that uses only incremental processing of a prefix to predict the word in a sentence . |
| Approach: | They propose a syntactic representation that commits to syntakic choices incrementally . they say the system can achieve 93.72 F1 on the Penn Treebank with as few as 5 bits per word . |
| Outcome: | The proposed representation achieves 93.72 F1 on the Penn Treebank with as few as 5 bits per word . the analysis of the representations shows they have interpretable features and deferred resolution of syntactic ambiguities. |