Evaluating the Quality of a Corpus Annotation Scheme Using Pretrained Language Models
Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024, Hybrid, Torino, İtalya, 20 - 25 Mayıs 2024, ss.6504-6514, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Basıldığı Şehir: Hybrid, Torino
- Basıldığı Ülke: İtalya
- Sayfa Sayıları: ss.6504-6514
- Anahtar Kelimeler: large language models, morphologically rich languages, treebank annotation, Turkish, Universal Dependencies
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Pretrained language models and large language models are increasingly used to assist in a great variety of natural language tasks. In this work, we explore their use in evaluating the quality of alternative corpus annotation schemes. For this purpose, we analyze two alternative annotations of the Turkish BOUN treebank, versions 2.8 and 2.11, in the Universal Dependencies framework using large language models. Using a suitable prompt generated using treebank annotations, large language models are used to recover the surface forms of sentences. Based on the idea that the large language models capture the characteristics of the languages, we expect that the better annotation scheme would yield the sentences with higher success. The experiments conducted on a subset of the treebank show that the new annotation scheme (2.11) results in a successful recovery percentage of about 2 points higher. All the code developed for this work is available at github.com/boun-tabi/eval-ud.