{"title":"Developing neutral linguistic resources for the implementation of an automatic transformational analyzer","authors":"Max Silberztein, Cristina Mota, Anabela Barreiro","doi":"10.12688/openreseurope.17990.1","DOIUrl":null,"url":null,"abstract":"Bakground The linguistic pursuit of describing natural languages stands as a commendable scientific endeavor, regardless of immediate software application prospects. It transcends mere documentation of possible sentences to establish connections between sentences derived from transformations. Methods Amid the dominance of Large Language Models (LLMs) in research and technology, which offer intriguing advancements in text generation, the approaches presented in this article confront challenges like opacity, limited human intervention, and adaptation difficulties inherent in LLMs. The alternative or complementary approaches highlighted here focus on the theoretical and methodological challenges of describing linguistic transformations and are firmly rooted in the field of linguistics, the science of language. We propose two solutions to address the problem of language transformations: (i) the procedural approach, which involves representing each transformation with a transducer, and (ii) the declarative method, which entails capturing all potential transformations in a single neutral grammar. Results These approaches simplify the generation of complex sentences from elementary ones and vice versa. Conclusion This work has benefited from research exchanges within the Multi3Generation COST Action (CA18231), and the resources produced can contribute to enhancing any language generation system.","PeriodicalId":74359,"journal":{"name":"Open research Europe","volume":"41 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open research Europe","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12688/openreseurope.17990.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Bakground The linguistic pursuit of describing natural languages stands as a commendable scientific endeavor, regardless of immediate software application prospects. It transcends mere documentation of possible sentences to establish connections between sentences derived from transformations. Methods Amid the dominance of Large Language Models (LLMs) in research and technology, which offer intriguing advancements in text generation, the approaches presented in this article confront challenges like opacity, limited human intervention, and adaptation difficulties inherent in LLMs. The alternative or complementary approaches highlighted here focus on the theoretical and methodological challenges of describing linguistic transformations and are firmly rooted in the field of linguistics, the science of language. We propose two solutions to address the problem of language transformations: (i) the procedural approach, which involves representing each transformation with a transducer, and (ii) the declarative method, which entails capturing all potential transformations in a single neutral grammar. Results These approaches simplify the generation of complex sentences from elementary ones and vice versa. Conclusion This work has benefited from research exchanges within the Multi3Generation COST Action (CA18231), and the resources produced can contribute to enhancing any language generation system.