Belinda Chiera


2023

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Using C-LARA to evaluate GPT-4’s multilingual processing
ChatGPT C-LARA-Instance | Belinda Chiera | Cathy Chua | Chadi Raheb | Manny Rayner | Annika Simonsen | Zhengkang Xiang | Rina Zviel-Girshin
Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association

We present a cross-linguistic study in which the open source C-LARA platform was used to evaluate GPT-4’s ability to perform several key tasks relevant to Computer Assisted Language Learning. For each of the languages English, Farsi, Faroese, Mandarin and Russian, we instructed GPT-4, through C-LARA, to write six different texts, using prompts chosen to obtain texts of widely differing character. We then further instructed GPT-4 to annotate each text with segmentation markup, glosses and lemma/part-of-speech information; native speakers hand-corrected the texts and annotations to obtain error rates on the different component tasks. The C-LARA platform makes it easy to combine the results into a single multimodal document, further facilitating checking of their correctness. GPT-4’s performance varied widely across languages and processing tasks, but performance on different text genres was roughly comparable. In some cases, most notably glossing of English text, we found that GPT-4 was consistently able to revise its annotations to improve them.

2022

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Using public domain resources and off-the-shelf tools to produce high-quality multimedia texts
Manny Rayner | Belinda Chiera | Cathy Chua
Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association

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Using LARA to create image-based and phonetically annotated multimodal texts for endangered languages
Branislav Bédi | Hakeem Beedar | Belinda Chiera | Nedelina Ivanova | Christèle Maizonniaux | Neasa Ní Chiaráin | Manny Rayner | John Sloan | Ghil’ad Zuckermann
Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages

We describe recent extensions to the open source Learning And Reading Assistant (LARA) supporting image-based and phonetically annotated texts. We motivate the utility of these extensions both in general and specifically in relation to endangered and archaic languages, and illustrate with examples from the revived Australian language Barngarla, Icelandic Sign Language, Irish Gaelic, Old Norse manuscripts and Egyptian hieroglyphics.