Outputs (2)

Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training (2020)
Conference Proceeding
Panagiaris, N., Hart, E., & Gkatzia, D. (2020). Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training. In Proceedings of the 13th International Conference on Natural Language Generation (41-51)

In this paper we consider the problem of optimizing neural Referring Expression Generation (REG) models with sequence level objectives. Recently reinforcement learning (RL) techniques have been adopted to train deep end-to-end systems to directly opt... Read More about Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training.

Generating Unambiguous and Diverse Referring Expressions   (2020)
Journal Article
Panagiaris, N., Hart, E., & Gkatzia, D. (2021). Generating Unambiguous and Diverse Referring Expressions  . Computer Speech and Language, 68, Article 101184. https://doi.org/10.1016/j.csl.2020.101184

Neural Referring Expression Generation (REG) models have shown promising results in generating expressions which uniquely describe visual objects. However, current REG models still lack the ability to produce diverse and unambiguous referring express... Read More about Generating Unambiguous and Diverse Referring Expressions  .