| 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. |
Similar Papers
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. |
Towards Turkish Abstract Meaning Representation (P19-2)
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| 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. |
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. |
| Approach: | They propose to expand the AMR project's lexicon of predicate senses to include entries for a growing set of constructions. |
| Outcome: | The proposed approach provides coverage for the annotation of certain types of constructions. |
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. |
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. |
Towards AMR-BR: A SemBank for Brazilian Portuguese Language (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a recent and prominent meaning representation with good acceptance and several applications in the Natural Language Processing area. |
| Approach: | They propose to build an AMR annotated corpus for Brazilian Portuguese using an alignment-based approach. |
| Outcome: | The proposed corpus is based on the Little Prince book, which went into the public domain and explored some language-specific annotation issues. |
CAMRA: Copilot for AMR Annotation (2023.emnlp-demo)
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| Challenge: | Abstract Meaning Representation (AMR) is a formalism for deep lexical semantic representation. |
| Approach: | They introduce a web-based tool for constructing AMR from natural language text . CAMRA incorporates AMR parser models as coding co-pilots . |
| Outcome: | The proposed tool is based on the prototyping of existing AMR editors and integrates Propbank roleset lookup as an autocomplete feature. |
Abstract Meaning Representation for Multi-Document Summarization (C18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation of natural language based on linguistic theory . |
| Approach: | They propose to use Abstract Meaning Representation (AMR) as a content representation. |
| Outcome: | The proposed framework is fully data-driven and flexible. |
Building a Broad Infrastructure for Uniform Meaning Representations (2024.lrec-main)
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Julia Bonn, Matthew J. Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajič, Kenneth Lai, James H. Martin, Skatje Myers, Alexis Palmer, Martha Palmer, Claire Benet Post, James Pustejovsky, Kristine Stenzel, Haibo Sun, Zdeňka Urešová, Rosa Vallejos, Jens E. L. Van Gysel, Meagan Vigus, Nianwen Xue, Jin Zhao
| Challenge: | This paper reports the first release of the UMR data set for six languages . it includes annotations for six different languages that vary greatly in terms of their linguistic properties and resource availability. |
| Approach: | They report the first release of the UMR data set for six languages . they describe on-going efforts to enlarge the data set and extend it to other languages - including Navajo, Navájo, and Sanapaná . |
| Outcome: | The first release of the UMR data set includes annotations for six languages . the language dataset is available for free and can be extended to other languages if needed . |
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. |