Sequence-to-sequence models have become the de facto standard for Abstract Meaning Representation (AMR) parsing due to their high-quality performance. However, these systems face efficiency challenges because of their large model size and computational time, which limit their accessibility within the research community. This paper aims to break down these barriers by introducing a novel linearization and system that significantly enhances the efficiency and accessibility of previous AMR parsers. First, we propose our novel Compact linearization that simplifies encoding, thereby reducing the number of tokens by between 40% and 50%. Second, we present CLAP, an innovative modular system that maintains the model’s high performance while achieving remarkable 80% reduction in training and inference times. Furthermore, CLAP is compatible with multiple autoregressive Language Models (LM) and tokenizers, such as BART, T5, and others. These advancements underscore the importance of optimizing sequence-to-sequence models in AMR parsing, thus democratizing access to high-quality semantic analysis. Our code is publicly available at https://github.com/SapienzaNLP/clap/.

Simone Conia, Edoardo Barba, Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Riccardo Orlando, Luigi Procopio, Roberto Navigli. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Conceptual representations of meaning have long been the general focus of Artificial Intelligence (AI) towards the fundamental goal of machine understanding, with innumerable efforts made in Knowledge Representation, Speech and Natural Language Processing, Computer Vision, inter alia. Even today, at the core of Natural Language Understanding lies the task of Semantic Parsing, the objective of which is to convert natural sentences into machine-readable representations. Through this paper, we aim to revamp the historical dream of AI, by putting forward a novel, all-embracing, fully semantic meaning representation, that goes beyond the many existing formalisms. Indeed, we tackle their key limits by fully abstracting text into meaning and introducing language-independent concepts and semantic relations, in order to obtain an interlingual representation. Our proposal aims to overcome the language barrier, and connect not only texts across languages, but also images, videos, speech and sound, and logical formulas, across many fields of AI.

A language-independent representation of meaning is one of the most coveted dreams in Natural Language Understanding.With this goal in mind, several formalisms have been proposed as frameworks for meaning representation in Semantic Parsing.And yet, the dependencies these formalisms share with respect to language-specific repositories of knowledge make the objective of closing the gap between high-and low-resourced languages hard to accomplish.In this paper, we present the Ba-belNet Meaning Representation (BMR), an interlingual formalism that abstracts away from language-specific constraints by taking advantage of the multilingual semantic resources of BabelNet and VerbAtlas.We describe the rationale behind the creation of BMR and put forward BMR 1.0, a dataset labeled entirely according to the new formalism.Moreover, we show how BMR is able to outperform previous formalisms thanks to its fully-semantic framing, which enables top-notch multilingual parsing and generation.We release the code at https: //github.com/SapienzaNLP/bmr.