Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System

One approach for multilingual data-to-text generation is to translate grammatical configurations upfront from the source language into each target language. These configurations are then used by a surface realizer and in document planning stages to generate output. In this paper, we describe a rule-based NLG implementation of this approach where the configuration is translated by Neural Machine Translation (NMT) combined with a one-time human review, and introduce a cross-language grammar dependency model to create a multilingual NLG system that generates text from the source data, scaling the generation phase without a human in the loop. Additionally, we introduce a method for human post-editing evaluation on the automatically translated text. Our evaluation on the SportSett:Basketball dataset shows that our NLG system performs well, underlining its grammatical correctness in translation tasks.

Integration of LLM Quality Assurance into an NLG System

In this paper, we present a system that uses a Large Language Model (LLM) to perform grammar and spelling correction as a component of Quality Assurance (QA) for texts generated by NLG systems, which is important for text production in real-world scenarios. Evaluating the results of the system on work-in-progress sports news texts in three languages, we show that it is able to deliver acceptable corrections.