Work

DCU-NLG-Small at the GEM’24 Data-to-Text Task: Rule-based generation and post-processing with T5-Base

Year: 2024

Type: conference-paper

Abstract: Our submission to the GEM data-to-text shared task aims to assess the quality of texts produced by the combination of a rule-based system with a language model of reduced size.Our system first uses a rule-based generator to convert input triples into semantically correct English text, and then a language model to paraphrase these texts to make them more fluent.The texts are translated to languages other than English with the NLLB machine translation system. 1

Source: International Conference on Natural Language Generation

DOI: 10.18653/v1/2024.inlg-genchal.9

Authors: Simon Mille, Mohammed Sabry, Anya Belz

Institution: Dublin City University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, Science Foundation Ireland, European Regional Development Fund, HORIZON EUROPE Marie Sklodowska-Curie Actions

Awards: G2563910032, ADAPT: Centre for Digital Content Platform Research, Multilingual Flexible Neuro-Symbolic Language Generation