Papers with LDC2017T10
AMR-To-Text Generation with Graph Transformer (2020.tacl-1)
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| Challenge: | Abstract meaning representation (AMR)-to-text generation is challenging task for natural language processing. |
| Approach: | They propose a graph-to-sequence model that directly encodes AMR graphs and learns node representations. |
| Outcome: | The proposed model outperforms the current state-of-the-art neural approach by 1.5 BLEU points on LDC2015E86 and 4.8 BLUE points on the LDC2017T10 and achieves new state- of-the art performance. |
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%). |
AMR Parsing via Graph-Sequence Iterative Inference (2020.acl-main)
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| 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. |
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity (2020.coling-main)
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| Challenge: | End-to-end neural data-totext generation has faced challenges generalizing to new domains and generating semantically consistent text. |
| Approach: | They propose a neural data-to-text generation system that makes minimal assumptions about the data representation and target domain. |
| Outcome: | The proposed system achieves state of the art results on four major D2T datasets with better semantic fidelity than the state-of-the-art methods. |
Structural Neural Encoders for AMR-to-text Generation (N19-1)
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| Challenge: | Abstract Meaning Representation (AMR) graphs are graphs, rather than trees, because they contain reentrant nodes with multiple parents. |
| Approach: | They propose to use sequence-to-sequence models that encode AMR graphs into vector representations to generate sentences from AMRs. |
| Outcome: | The proposed model outperforms tree encoders in the AMR-to-text generation task by 24.40 points. |
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. |
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)
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| Challenge: | Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. |
| Approach: | They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model. |
| Outcome: | The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores . |