William Lamb


2023

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Building a dual dataset of text- and image-grounded conversations and summarisation in Gàidhlig (Scottish Gaelic)
David M. Howcroft | William Lamb | Anna Groundwater | Dimitra Gkatzia
Proceedings of the 16th International Natural Language Generation Conference

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.

2022

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Proceedings of the 4th Celtic Language Technology Workshop within LREC2022
Theodorus Fransen | William Lamb | Delyth Prys
Proceedings of the 4th Celtic Language Technology Workshop within LREC2022

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Handwriting recognition for Scottish Gaelic
William Lamb | Beatrice Alex | Mark Sinclair
Proceedings of the 4th Celtic Language Technology Workshop within LREC2022

Like most other minority languages, Scottish Gaelic has limited tools and resources available for Natural Language Processing research and applications. These limitations restrict the potential of the language to participate in modern speech technology, while also restricting research in fields such as corpus linguistics and the Digital Humanities. At the same time, Gaelic has a long written history, is well-described linguistically, and is unusually well-supported in terms of potential NLP training data. For instance, archives such as the School of Scottish Studies hold thousands of digitised recordings of vernacular speech, many of which have been transcribed as paper-based, handwritten manuscripts. In this paper, we describe a project to digitise and recognise a corpus of handwritten narrative transcriptions, with the intention of re-purposing it to develop a Gaelic speech recognition system.

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Developing Automatic Speech Recognition for Scottish Gaelic
Lucy Evans | William Lamb | Mark Sinclair | Beatrice Alex
Proceedings of the 4th Celtic Language Technology Workshop within LREC2022

This paper discusses our efforts to develop a full automatic speech recognition (ASR) system for Scottish Gaelic, starting from a point of limited resource. Building ASR technology is important for documenting and revitalising endangered languages; it enables existing resources to be enhanced with automatic subtitles and transcriptions, improves accessibility for users, and, in turn, encourages continued use of the language. In this paper, we explain the many difficulties faced when collecting minority language data for speech recognition. A novel cross-lingual approach to the alignment of training data is used to overcome one such difficulty, and in this way we demonstrate how majority language resources can bootstrap the development of lower-resourced language technology. We use the Kaldi speech recognition toolkit to develop several Gaelic ASR systems, and report a final WER of 26.30%. This is a 9.50% improvement on our original model.

2014

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Developing an Automatic Part-of-Speech Tagger for Scottish Gaelic
William Lamb | Samuel Danso
Proceedings of the First Celtic Language Technology Workshop