Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters (2023.eacl-main)
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| Challenge: | Recent approaches to text generation from Abstract Meaning Representation (AMR) have been based on neural-centered encoderdecoder architectures. |
| Approach: | They propose a structure-aware adapter which injects the input graph connectivity within PLMs using Graph Neural Networks. |
| Outcome: | The proposed adapter is robust to a variety of approaches and can be used to generate Graph-to-Text representations. |
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Structural Adapters in Pretrained Language Models for AMR-to-Text Generation (2021.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) have advanced graph-to-text generation, but efficient encoding of graph structure is challenging because of the nature of the data. |
| Approach: | They propose a method to encode graph structure into pretrained language models by training only graph structure-aware adapter parameters. |
| Outcome: | The proposed method outperforms the state-of-the-art on two AMR-to-text datasets, training only 5.1% of the adapter parameters. |
Graph Pre-training for AMR Parsing and Generation (2022.acl-long)
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| Challenge: | Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. |
| Approach: | They propose two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-tuning to improve structure awareness. |
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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. |
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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 . |
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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. |
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Incorporating Graph Information in Transformer-based AMR Parsing (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic graph abstraction for text representations. |
| Approach: | They propose a model and method that incorporates graph information into the learned representations of AMR by word-to-node alignment. |
| Outcome: | The proposed model improves AMR parsing performance by embedding graph information into the encoder at training time. |
GPT-too: A Language-Model-First Approach for AMR-to-Text Generation (2020.acl-main)
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Manuel Mager, Ramón Fernandez Astudillo, Tahira Naseem, Md Arafat Sultan, Young-Suk Lee, Radu Florian, Salim Roukos
| Challenge: | Existing approaches to generating text from AMRs focus on training sequence-to-sequence or graph-tosequent models on annotated data. |
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Guiding AMR Parsing with Reverse Graph Linearization (2023.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a sentence. |
| Approach: | They propose a new framework that allows for reversed linearization of AMR graphs . they propose to combine sequence-to-sequence approaches with a linearized graph . |
| Outcome: | The proposed framework outperforms the best AMR parser by 0.8 and 0.5 Smatch scores on the AMR 2.0 and AMR 3.0 datasets. |
The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation (2024.naacl-short)
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| Challenge: | Continuous-output neural machine translation models are trained to predict the continuous representation based on distances between vectors. |
| Approach: | They propose a continuous-output neural machine translation (CoNMT) approach that uses random output embeddings to outperform laboriously pre-trained models. |
| Outcome: | The proposed strategy outperforms pre-trained embeddings on large datasets and is strongest for rare words due to the geometry of their embedders. |
Analyzing the Role of Semantic Representations in the Era of Large Language Models (2024.naacl-long)
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Zhijing Jin, Yuen Chen, Fernando Gonzalez Adauto, Jiarui Liu, Jiayi Zhang, Julian Michael, Bernhard Schölkopf, Mona Diab
| Challenge: | Existing studies show the benefits of semantic representations in NLP tasks . Existing work using AMR is concerned with trainable models . |
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