Building a dual dataset of text- and image-grounded conversations and summarisation in Gàidhlig ( S cottish G aelic) (2023)

Abstract: Gàidhlig (Scottish Gaelic; gd) is spoken by about 57k people in Scotland, but remains an under-resourced language with respect to natural language processing in general and natural language generation (NLG) in particular. To address this gap, we developed the first datasets for Scottish Gaelic NLG, collecting both conversational and summarisation data in a single setting. Our task setup involves dialogues between a pair of speakers discussing museum exhibits, grounding the conversation in images and texts. Then, both interlocutors summarise the dialogue resulting in a secondary dialogue summarisation dataset. This paper presents the dialogue and summarisation corpora, as well as the software used for data collection. The corpus consists of 43 conversations (13.7k words) and 61 summaries (2.0k words), and will be released along with the data collection interface.

## introduction the preservation of minority languages and the development of natural language processing (nlp) systems in low-resource settings have gained increasing attention in recent years @xcite @xcite , fueled by efforts to safeguard linguistic diversity and cultural heritage @xcite as well as efforts to create inclusive and fairer systems @xcite . one such minority language is scottish gaelic, which despite being a recognised national language under the european charter for regional or minority languages and a rise in gaelic-medium education, faces challenges in terms of linguistic resources for the development of natural language generation (nlg) systems. to bridge this gap, we present a 1 https://www.scotlandscensus.gov.uk/ census-results/at-a-glance/languages/ 2 the datasets, along with code for the interface, are available at https://github.com/napiernlp/sgge . to construct the dataset, we adopted a task setup centered around dialogues between proficient speakers, engaging them in conversations about museum exhibits. by grounding the conversations in images and texts, we aimed to create a contextually rich conversational dataset. subsequently, each participant summarised the dialogue, resulting in a secondary dialogue summarisation dataset. the primary contributions of this work are twofold: the dual-corpus, comprising the dialogue and dialogue summarisation datasets; and the software employed for data collection. these contributions are pivotal in advancing research in nlg for scottish gaelic and hold significant potential for future developments in the field. moreover, this paper sheds light on the challenges and complexities encountered when gathering high-quality dialogue datasets involving native speakers of low-resource languages. by addressing these challenges and presenting a robust corpus and data collection methodology, this work enables further progress in lowresource nlg, within and beyond scottish gaelic. ## corpus collection we ground our data collection in multi-modal sources about exhibits found at the national museum of scotland. participants discuss each exhibit through a chat interface based on slurk @xcite . ## corpus analysis 4 demographics we recruited 24 participants, 13 of whom live in the highlands & islands and 6 of whom live in the central belt in scotland. three are from the lowlands, with 1 living outside of scotland and 1 in the south & borders. most report speaking (19) and hearing (20) scottish gaelic daily, with 17 also reading it daily and 13 writing daily. all but one participant used the language across all modalities at least monthly. eighteen started learning the language before the age of 25. we had only one participant under 25 years old, with participants otherwise fairly evenly split among 25-34, 35-44, 45-54, and 55 or older. ## discussion & conclusion recruitment was a major obstacle to our data collection (cf. sec. 2.3), with this kind of study being more akin to linguistic field work or a psycholinguistic lab experiment than typical crowdsourcing tasks. our research greatly benefited from the expertise of our gaelic specialist, who contributed invaluable cultural and linguistic knowledge. this first dataset for scottish gaelic nlg is of the same order of magnitude as nlg datasets for english just a decade ago (e.g. @xcite restaurant corpus of 5k utterances), providing a solid starting point for developing prototype systems. indeed, we are currently exploring response generation for grounded question answering, dialogue summarisation, and text summarisation in scottish gaelic using this dataset. beyond the dataset, our experimental interface enables other researchers to build multipurpose datasets combining summarisation and conversation and/or grounding in text and images, especially for low-resource languages. we look forward to expanding this corpus in the future and enabling others to develop more datasets for more low-resource languages.