Developing neutral linguistic resources for the implementation of an automatic transformational analyzer

Max Silberztein, Cristina Mota, Anabela Barreiro
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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.
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开发中性语言资源以实施自动转换分析器
背景 描述自然语言的语言学追求是一项值得称道的科学努力,而不考虑直接的软件应用前景。它超越了仅仅记录可能的句子的范畴,而是要建立由转换而来的句子之间的联系。方法 大语言模型(LLM)在研究和技术领域占据主导地位,为文本生成提供了引人入胜的进步,但本文介绍的方法却面临着 LLM 固有的不透明性、有限的人工干预和适应困难等挑战。本文强调的替代或补充方法侧重于描述语言转换的理论和方法挑战,并牢牢扎根于语言学这一语言科学领域。我们提出了两种解决方案来解决语言转换问题:(i) 程序性方法,即用转换器来表示每种转换;(ii) 声明性方法,即在一个中性语法中捕捉所有潜在的转换。结果 这些方法简化了从基本句子生成复杂句子的过程,反之亦然。结论 这项工作得益于多重生成 COST 行动(CA18231)中的研究交流,所产生的资源有助于增强任何语言生成系统。
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