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Cardamom Seminar Series #21 – Prof Kevin Scannell (Saint Louis University)
April 24 @ 5:00 pm – 6:00 pm IST
Towards explainable AI for Irish grammatical error correction
The Unit for Linguistic Data at the Insight SFI Research Centre for Data Analytics / Data Science Institute, University of Galway, is delighted to welcome Prof Kevin Scannell of Saint Louis University as the next speaker in our seminar series. He will talk about the importance of grammatical error correction focused on Irish. Register here.
Grammatical error correction is an important end-user application of natural language processing. In recent years, approaches using large language models have led to improved performance on this task, at least for English and a few other well-resourced languages. Nevertheless, it remains challenging to build systems that (1) provide results that are sufficiently reliable for end-users and (2) give some explanation for errors that they detect for the benefit of language learners. I will discuss recent progress on this problem for the Irish language, focusing on an important subset of errors involving the so-called “initial mutations” found in Irish and the other Celtic languages. The primary challenge is assembling a large enough dataset for training — we make use of both synthetic data produced with the help of an Irish dependency parser, as well as error examples mined from Wikipedia edit logs.
About the Speaker:
Kevin Scannell is a Professor of Mathematics and Computer Science at Saint Louis University, where he has taught since 1998. His main interest is the development of technology that helps speakers of indigenous and minority languages use their language online, with a particular focus on Irish and the other Celtic languages.
The seminar series is led by the Cardamom project team. The Cardamom project aims to close the resource gap for minority and under-resourced languages using deep-learning-based natural language processing (NLP) and exploiting similarities of closely related languages. The project further extends this idea to historical languages, which can be considered closely related to their modern form. It aims to provide NLP through both space and time for languages that current approaches have ignored.