Sentence Detailing and Its Applications

ŞAHİN F., AMASYALI M. F.

2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023, Sivas, Türkiye, 11 - 13 Ekim 2023 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/asyu58738.2023.10296831
  • Basıldığı Şehir: Sivas
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: commonsense knowledge, data augmentation, deep learning, natural language generation, natural language processing, text-to-text transformer
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

The purpose of this study is to observe whether there is a performance improvement when we train the multilingual Text-to-Text Transfer Transformer (mT5), which is a transformer model in Natural Language Processing (NLP), with a dataset having sentences constructed using a set of given words as a pre-process before we train the model with a text-from-title dataset directly. Given words were considered as concept-set and the model was expected to learn the concept like commonsense knowledge from news to generate appropriate sentences after being trained with title-to-text dataset as well. We named this method 'Sentence Detailing' due to its feature of generating sentences by adding details to a set of words. In addition to the text generation from title, we also examined this method under the topic of data augmentation.