Knowledge-Grounded Dialogue Generation with Term-level De-noising

1 code implementation • Findings (ACL) 2021 • Wen Zheng, Natasa Milic-Frayling, Ke Zhou

Dialogue Generation

Contextual Knowledge Learning For Dialogue Generation

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.

Dialogue Generation Response Generation

LAraBench: Benchmarking Arabic AI with Large Language Models

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.

Benchmarking Few-Shot Learning +1

Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation

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.

Dialogue Generation Retrieval +1

Predicting News Values from Headline Text and Emotions

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.

BIG-bench Machine Learning Recommendation Systems