| Challenge: | Abstract Meaning Representation (AMR) abstracts away from syntactic features such as word order and does not annotate every constituent in a sentence. |
| Approach: | They have built a first Turkish AMR corpus by hand-annotating 100 sentences from the novel "The Little Prince" they will use the results to prepare a Turkish AML annotation specification for future annotators. |
| Outcome: | The results of the study compare Turkish AMRs with English AMR annotations . the proposed framework is expected to be used in training future annotators. |
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Cross-Lingual Abstract Meaning Representation Parsing (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) research has focused on English . Qualitative analysis shows that the new parsers overcome structural differences between the languages. |
| Approach: | They propose to use an AMR parser for English and parallel corpora to learn AMR for Italian, Spanish, German and Chinese. |
| Outcome: | The proposed method overcomes structural differences between the target languages and requires no gold standard data. |
Annotating Abstract Meaning Representations for Spanish (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation language for natural language processing. |
| Approach: | They propose a method that would lay the groundwork for building a large semantic bank for Spanish . they propose to use a database to annotate AMRs for other languages . |
| Outcome: | The proposed method would lay the groundwork for building a large semantic bank for Spanish and guide those who would like to implement it for other languages. |
A Corpus of German Abstract Meaning Representation (DeAMR) (2024.lrec-main)
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| Challenge: | Abstract Meaning Representations (AMRs) are semantic graphs that abstract away from surface syntax and capture the meaning of who does what to whom in a sentence. |
| Approach: | They propose to use German Abstract Meaning Representation (Deutsche AMR) to represent the structure and semantics of German. |
| Outcome: | The proposed framework is based on an annotated corpus of 400 DeAMR in German and is validated through inter-annotator agreement. |
A Structured Syntax-Semantics Interface for English-AMR Alignment (N18-1)
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| Challenge: | Abstract Meaning Representation (AMR) annotations do not require explicit mapping between elements of an AMR and the corresponding elements of the sentence that evoke them. |
| Approach: | They devised an expressive framework to align AMR graphs to dependency graphs . their framework explains how 97% of AMR edges are evoked by words or syntax . |
| Outcome: | The proposed framework explains how 97% of AMR edges are evoked by words or syntax. |
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 . |
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. |
| Outcome: | The proposed approach generates meaning-preserving syntactic paraphrases of the same graph, as judged by humans. |
Abstract Meaning Representation of Constructions: The More We Include, the Better the Representation (L18-1)
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Claire Bonial, Bianca Badarau, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Tim O’Gorman, Martha Palmer, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) uses a flexible pattern or template of multiple lexical items to provide semantic representation of certain constructions. |
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| Outcome: | The proposed approach provides coverage for the annotation of certain types of constructions. |
AMR Alignment for Morphologically-rich and Pro-drop Languages (2022.acl-srw)
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| Challenge: | Existing AMR aligners for English are not well suited for many languages where many concepts appear from morphologically-semantic elements. |
| Approach: | They propose to use a tree traversal approach to align AMR concepts from morphemes in a Turkish language. |
| Outcome: | The proposed aligner outperforms the existing aligners for English and Portuguese in terms of precision, recall and F1 score. |
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. |
Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing (2021.findings-acl)
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| Challenge: | Existing work on meaning representations for English and other languages finds that concepts in their predicted AMR graphs are less specific. |
| Approach: | They propose a cross-lingual AMR parser that can predict more precise concepts by translating translated texts and non-English texts. |
| Outcome: | The proposed model surpasses state-of-the-art parser by 10.6 points on Smatch F1 score. |