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arXiv · 2501.16135

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

Andreas Madsack·Johanna Heininger·Adela Schneider·Ching-Yi Chen·Christian Eckard·Robert Weißgraeber
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Abstract

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.

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Andreas Madsack, Johanna Heininger, Adela Schneider, Ching-Yi Chen, Christian Eckard, Robert Weißgraeber. 2025-01-27. Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System. https://arxiv.org/abs/2501.16135

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