Anabela Barreiro

This article explores the concept of paraphrasing within computational linguistics, seeking to enrich its understanding by drawing parallels with translation studies and especially machine translation. It highlights the existence of two distinct yet related tasks: paraphrase generation and paraphrase detection, as well as points out the many (sometimes implicit) contact points in evaluation in both translation and paraphrasing. We claim that the concept of near-synonymy or near- equivalence is a shared concern of both disciplines, and its formalization should be pursued.

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.

Natural Language Generation, a well-established research area within the field of Natural Language Processing, has achieved significant milestones such as in machine translation and paraphrasing. Large Language Models, while improving text quality, show varying effectiveness across different linguistic registers and cultural contexts, raising concerns mostly due to (i) difficulties in understanding the internal workings of the models; (ii) difficulties in understanding the generative processes, which are not transparent; (iii) difficulties in human intervention in the generative function; (iv) propensity for hallucination and incorrect information content, (v) potential irresponsible use of extensive resources from unidentified sources, and (vi) the risk of misuse, among the most obvious hurdles. This article has three main goals: (1) suggest a linguistic approach to Natural Language Generation, (2) explore the broad language spectrum, including formal to informal styles and objective to subjective language, among others, and (3) propose language models inspired by the Logos Model for enhanced transparency, traceability, and customization. The Logos Model is at the core of the best-documented pioneer commercial machine translation system called Logos, which preludes the history of Generative Artificial Intelligence. The open-source version, OpenLogos, offers valuable resources for machine translation, paraphrasing, abstractive summarization, and various other tasks related to Natural Language Generation, and it serves as an excellent resource for training researchers across multiple fields associated with Artificial Intelligence. The OpenLogos resources were made publicly available through the Multi3Generation COST Action (CA18231).

Large Language Models (LLMs) offer advanced text generation capabilities, sometimes surpassing human abilities. However, their use without proper expertise poses significant challenges, particularly in educational contexts. This article explores different facets of natural language generation (NLG) within the educational realm, assessing its advantages and disadvantages, particularly concerning LLMs. It addresses concerns regarding the opacity of LLMs and the potential bias in their generated content, advocating for transparent solutions. Therefore, it examines the feasibility of integrating OpenLogos expert-crafted resources into language generation tools used for paraphrasing and translation. In the context of the Multi3Generation COST Action (CA18231), we have been emphasizing the significance of incorporating OpenLogos into language generation processes, and the need for clear guidelines and ethical standards in generative models involving multilingual, multimodal, and multitasking capabilities. The Multi3Generation initiative strives to progress NLG research for societal welfare, including its educational applications. It promotes inclusive models inspired by the Logos Model, prioritizing transparency, human control, preservation of language principles and meaning, and acknowledgment of the expertise of resource creators. We envision a scenario where OpenLogos can contribute significantly to inclusive AI-supported education. Ethical considerations and limitations related to AI implementation in education are explored, highlighting the importance of maintaining a balanced approach consistent with traditional educational principles. Ultimately, the article advocates for educators to adopt innovative tools and methodologies to foster dynamic learning environments that facilitate linguistic development and growth.

In the framework of Multi3Generation COST Action (CA18231), we have provided the OpenLogos tools and resources to the community with the belief that (i) they can be beneficial to Early Research Career researchers and (ii) they can also assist startup companies with limited resources in initiating endeavors to create their Natural Language Generation, Machine Translation, Paraphrasing or other Natural Language Processing systems. In this article, we explore OpenLogos capabilities in an industry case scenario. We provide historical background on the proven importance of the Logos commercial system in shaping the documentation process of a multinational. Understanding the historical significance of Machine Translation is crucial for grasping our present position in Generative Language Models and our role in the domain of Generative Artificial Intelligence. We explore how novel Custom Generative Language Models, drawing inspiration from the Logos Model, can prove beneficial across various industries worldwide.

The article emphasizes the critical importance of language generation today, particularly focusing on three key aspects: Multitasking, Multilinguality, and Multimodality, which are pivotal for the Natural Language Generation community. It delves into the activities conducted within the Multi3Generation COST Action (CA18231) and discusses current trends and future perspectives in language generation.

The purpose of this article is to highlight the critical importance of language generation today. In particular, language generation is explored from the following three aspects: multi-modality, multilinguality, and multitask, which all of them play crucial role for Natural Language Generation (NLG) community. We present the activities conducted within the Multi3Generation COST Action (CA18231), as well as current trends and future perspectives for multitask, multilingual and multimodal language generation.