Spanish Abstract Meaning Representation: Annotation of a General Corpus

• URL: http://arxiv.org/abs/2204.07663v1
• Date: Fri, 15 Apr 2022 22:26:04 GMT
• Title: Spanish Abstract Meaning Representation: Annotation of a General Corpus
• Abstract summary: We build on previous work proposing the annotation of AMR in Spanish, which resulted in the release of 50 Spanish AMR annotations for the fictional text "The Little Prince" Our approach to annotation makes use of Spanish rolesets from the AnCora-Net lexicon and extends English AMR with semantic features specific to Spanish.
• Score: 7.837003011850223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Abstract Meaning Representation (AMR) formalism, designed originally for English, has been adapted to a number of languages. We build on previous work proposing the annotation of AMR in Spanish, which resulted in the release of 50 Spanish AMR annotations for the fictional text "The Little Prince." In this work, we present the first sizable, general annotation project for Spanish Abstract Meaning Representation. Our approach to annotation makes use of Spanish rolesets from the AnCora-Net lexicon and extends English AMR with semantic features specific to Spanish. In addition to our guidelines, we release an annotated corpus (586 annotations total, for 486 unique sentences) of multiple genres of documents from the "Abstract Meaning Representation 2.0 - Four Translations" sembank. This corpus is commonly used for evaluation of AMR parsing and generation, but does not include gold AMRs; we hope that providing gold annotations for this dataset can result in a more complete approach to cross-lingual AMR parsing. Finally, we perform a disagreement analysis and discuss the implications of our work on the adaptability of AMR to languages other than English.

Related papers

• DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis [57.70022214686838]
  DimABSA is the first multilingual, dimensional ABSA resource annotated with both traditional ABSA elements and VA scores.<n>This resource contains 76,958 aspect instances across 42,590 sentences, spanning six languages and four domains.
  arXiv  Detail & Related papers  (2026-01-30T14:30:35Z)
• ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue [4.909170127604826]
  We present our work to build a French semantic corpus by annotating French dialogue in Abstract Meaning Representation (AMR)<n>Specifically, we annotate the DinG corpus, consisting of transcripts of spontaneous French dialogues recorded during the board game Catan.<n>As AMR has insufficient coverage of the dynamics of spontaneous speech, we extend the framework to better represent spontaneous speech and sentence structures specific to French.
  arXiv  Detail & Related papers  (2025-08-18T10:57:44Z)
• MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detection [3.6811136816751513]
  We introduce MASSIVE-AMR, a dataset with more than 84,000 text-to-graph annotations. AMR graphs for 1,685 information-seeking utterances mapped to 50+ typologically diverse languages. Results shed light on persistent issues using LLMs for structured parsing.
  arXiv  Detail & Related papers  (2024-05-29T17:17:22Z)
• Analyzing the Role of Semantic Representations in the Era of Large Language Models [104.18157036880287]
  We investigate the role of semantic representations in the era of large language models (LLMs) We propose an AMR-driven chain-of-thought prompting method, which we call AMRCoT. We find that it is difficult to predict which input examples AMR may help or hurt on, but errors tend to arise with multi-word expressions.
  arXiv  Detail & Related papers  (2024-05-02T17:32:59Z)
• "You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representation [60.863629647985526]
  We examine the successes and limitations of the GPT-3, ChatGPT, and GPT-4 models in analysis of sentence meaning structure. We find that models can reliably reproduce the basic format of AMR, and can often capture core event, argument, and modifier structure. Overall, our findings indicate that these models out-of-the-box can capture aspects of semantic structure, but there remain key limitations in their ability to support fully accurate semantic analyses or parses.
  arXiv  Detail & Related papers  (2023-10-26T21:47:59Z)
• Widely Interpretable Semantic Representation: Frameless Meaning Representation for Broader Applicability [10.710058244695128]
  This paper presents a novel semantic representation, WISeR, that overcomes challenges for Abstract Meaning Representation (AMR) Despite its strengths, AMR is not easily applied to languages or domains without predefined semantic frames. We create a new corpus of 1K English dialogue sentences in both WISeR and AMR WISeR.
  arXiv  Detail & Related papers  (2023-09-12T17:44:40Z)
• Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation [70.58243648754507]
  We introduce a new method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR) Compared with the original textual input, AMR is a structured semantic representation that presents the core concepts and relations in a sentence explicitly and unambiguously. Experiment results show that retrofitting multilingual sentence embeddings with AMR leads to better state-of-the-art performance on both semantic similarity and transfer tasks.
  arXiv  Detail & Related papers  (2022-10-18T11:37:36Z)
• Multilingual Word Sense Disambiguation with Unified Sense Representation [55.3061179361177]
  We propose building knowledge and supervised-based Multilingual Word Sense Disambiguation (MWSD) systems. We build unified sense representations for multiple languages and address the annotation scarcity problem for MWSD by transferring annotations from rich-sourced languages to poorer ones. Evaluations of SemEval-13 and SemEval-15 datasets demonstrate the effectiveness of our methodology.
  arXiv  Detail & Related papers  (2022-10-14T01:24:03Z)
• Persian Abstract Meaning Representation [3.2689702143620147]
  We elaborate on Persian Abstract Meaning Representation (PAMR) annotation specifications, based on which we annotated the Persian translation of "The Little Prince" as the first gold standard for Persian AMR. We describe how some Persian-specific syntactic constructions would result in different AMR annotations.
  arXiv  Detail & Related papers  (2022-05-16T14:25:43Z)
• Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation [55.117031558677674]
  We study different techniques for automatically generating AMR annotations. Our models trained on gold AMR with silver (machine translated) sentences outperform approaches which leverage generated silver AMR. Our models surpass the previous state of the art for German, Italian, Spanish, and Chinese by a large margin.
  arXiv  Detail & Related papers  (2021-09-08T17:55:46Z)
• Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing [88.08581016329398]
  We argue that the misprediction of concepts is due to the high relevance between English tokens and AMR concepts. We introduce bilingual input, namely the translated texts as well as non-English texts, in order to enable the model to predict more accurate concepts.
  arXiv  Detail & Related papers  (2021-06-09T05:14:54Z)
• Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing [10.495114898741205]
  We develop models that project sentences from various languages onto their AMRs to capture their essential semantic structures. In this paper, we revisit a simple two-step base-line, and enhance it with a strong NMT system and a strong AMR. Our experiments show that T+P outperforms a recent state-of-the-art system across all tested languages.
  arXiv  Detail & Related papers  (2021-06-08T17:52:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.