AMR-TST: Abstract Meaning Representation-based Text Style Transfer (2023.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. |
| Approach: | They propose an AMR-based text style transfer technique that converts source text to an AML graph and generates transferred text based on the AMR graph modified by a TST policy named style rewriting. |
| Outcome: | The proposed method achieves state-of-the-art results compared with baseline models in automatic and human evaluations. |
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T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation (2022.emnlp-main)
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| Challenge: | Unavailability of parallel corpora for training text style transfer models is a challenge but common . a large corpus of parallel data is not available for text style transfers . |
| Approach: | They propose to use AMR as an intermediate style agnostic representation to train TST models. |
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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. |
| Approach: | They propose a strong pre-trained language model with cycle consistency-based re-scoring to generate AMR text. |
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AMR-DA: Data Augmentation by Abstract Meaning Representation (2022.findings-acl)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation for NLP/NLU. |
| Approach: | They propose to use AMR-DA for data augmentation in NLP . they use sentence-level techniques like back translation and token-level methods like EDA . |
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Factorising AMR generation through syntax (N19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic annotation framework which abstracts away from the surface form of text to capture the core 'who did what to whom' structure. |
| Approach: | They propose to decompose the generation process into two steps: first generate a syntactic structure, and then generate the surface form. |
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Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation (2024.eacl-long)
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| Challenge: | Abstract Meaning Representation (AMR) can be used to reduce translationese in text . if translationeses are not addressed in training or test sets, evaluation scores can be overinflated . |
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XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques (2020.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a popular formalism of natural language. |
| Approach: | They develop a cross-lingual AMR parser that can be trained on the produced data . they use transfer learning techniques to produce automatic AMR annotations across languages . |
| Outcome: | The proposed parser significantly surpasses those reported in Chinese, German, Italian and Spanish. |
The Role of Reentrancies in Abstract Meaning Representation Parsing (2020.findings-emnlp)
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| Challenge: | Abstract Meaning Representation (AMR) parsers make errors with respect to reentrancies, which complicates AMR parsing and requires specific transitions. |
| Approach: | They propose to categorize the types of errors AMR parsers make with respect to reentrancies and find that correcting these errors provides an in-crease of up to 5% Smatch in parsing perfor- mance and 20% in reen- trancy prediction. |
| Outcome: | The proposed formalism can predict reentrancies with 5% accuracy and 20% accuracy. |
Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation (2022.emnlp-main)
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| Challenge: | Existing studies on multilingual sentence embeddings focus on cross-lingual semantic textual similarity and transfer tasks. |
| Approach: | They propose a method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR) . they compare existing multi-lingual sentence embedded with AMR and improve their versions by reducing the surface variations across different languages and expressions. |
| Outcome: | The proposed method improves state-of-the-art multilingual sentence embeddings on transfer tasks and semantic textual similarity tests. |
A Partially Rule-Based Approach to AMR Generation (N19-3)
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| Challenge: | Abstract Meaning Representation (AMR) is a representation of a sentence as a labeled graph . because of these abstractions, it can be difficult to generate from AMR back to a fluent English sentence . |
| Approach: | They propose a new approach to generating English text from Abstract Meaning Representation (AMR) it is largely rule-based, supplemented by a language model and simple statistical linearization models . they also address difficulties of automatically evaluating AMR generation systems . |
| Outcome: | The proposed approach produces a fluent English sentence with a high quality . it is difficult to generate from an AMR back to a sentence which preserves original meaning . |
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. |