W eb NLG Challenge 2023: Domain Adaptive Machine Translation for Low-Resource Multilingual RDF -to-Text Generation ( W eb NLG 2023) (2023)

Abstract: This paper presents our submission to the WebNLG Challenge 2023 for generating text in several low-resource languages from RDF-triples. Our submission focuses on using machine translation for generating texts in Irish, Maltese, Welsh and Russian. While a simple and straightfoward approach, recent works have shown that using monolingual models for inference for multilingual tasks with the help of machine translation (translate-test) can out-perform multilingual models and training multilingual models on machine-translated data (translate-train) through careful tuning of the MT component. Our results show that this approach demonstrates competitive performance for this task even with limited data.

## introduction the multilingual rdf-to-text generation task involves generating verbalizations of rdf triples in different languages. it represents an important problem, as it can allow for the rapid generation and enrichment of informative texts in different languages by leveraging existing knowledge graphs and databases. this is of particular relevance for low-resource languages which are typically less represented. recent methods for rdf-to-text generation rely on neural methods, which require availability of high quality data for training. however, this is challenging for low-resource languages due to the lack of such data. manual curation of such datasets requires expert human supervision, and is expensive and time-consuming. thus, utilizing machine translated data available in different languages is naturally motivated. the translate-train method relies of translating the training data to the target language, while the translate-test method translate the test data to english and using a monolingual model for inference. recent works have shown that through careful adaption of the mt component, the translate-test method can perform significantly better than previously shown results, even outperforming translatetrain and multilingual models across different classification tasks @xcite . our submission investigates the performance of translate-test method for generation. in particular, we use domain adaption to improve the mt component and generate high quality texts in the target language with very little training data. we first generate text in english using a monolingual, english generation model, and then translate the generated text to the target text using the mt component. ## results