no code implementations • 29 May 2023 • Wen Zheng, Natasa Milic-Frayling, Ke Zhou
We guide the model training through a Contextual Knowledge Learning (CKL) process which involves Latent Vectors for context and knowledge, respectively.
no code implementations • 24 May 2023 • Ahmed Abdelali, Hamdy Mubarak, Shammur Absar Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Yousseif Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, Firoj Alam
Our findings provide valuable insights into the applicability of LLMs for Arabic NLP and speech processing tasks.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Wen Zheng, Natasa Milic-Frayling, Ke Zhou
This paper is concerned with improving dialogue generation models through injection of knowledge, e. g., content relevant to the post that can increase the quality of responses.
no code implementations • WS 2017 • Maria Pia di Buono, Jan {\v{S}}najder, Bojana Dalbelo Ba{\v{s}}i{\'c}, Goran Glava{\v{s}}, Martin Tutek, Natasa Milic-Frayling
We present a preliminary study on predicting news values from headline text and emotions.