CiViL: Common-sense- and Visual-enhanced natural Language generation

Second Workshop on Natural Language Generation for Human-Robot Interaction (2020)
Presentation / Conference Contribution
Buschmeier, H., Ellen Foster, M., & Gkatzia, D. (2020, March). Second Workshop on Natural Language Generation for Human-Robot Interaction. Presented at HRI '20: ACM/IEEE International Conference on Human-Robot Interaction, Cambridge

This workshop is the second in a series bringing together the Natural Language Generation and Human-Robot Interaction communities to discuss topics of mutual interest with the goal of developing an HRI-inspired NLG shared task. The workshop website i... Read More about Second Workshop on Natural Language Generation for Human-Robot Interaction.

Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definition (2020)
Presentation / Conference Contribution
Howcroft, D., Belz, A., Clinciu, M., Gkatzia, D., Hasan, S. A., Mahamood, S., Mille, S., van Miltenburg, E., Santhanam, S., & Rieser, V. (2020, December). Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definition. Presented at International Conference on Natural Language Generation (INLG 2020), Dublin, Ireland

Human assessment remains the most trusted form of evaluation in NLG, but highly diverse approaches and a proliferation of different quality criteria used by researchers make it difficult to compare results and draw conclusions across papers, with adv... Read More about Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definition.

Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training (2020)
Presentation / Conference Contribution
Panagiaris, N., Hart, E., & Gkatzia, D. (2020, December). Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training. Presented at International Conference on Natural Language Generation (INLG 2020), Dublin, Ireland

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.

Commonsense-enhanced Natural Language Generation for Human-Robot Interaction (2020)
Presentation / Conference Contribution
Gkatzia, D. (2020, December). Commonsense-enhanced Natural Language Generation for Human-Robot Interaction. Presented at 2nd Workshop on Natural Language Generation for Human-Robot Interaction (HRI 2020), Online

Commonsense is vital for human communication, as it allows us to make inferences without explicitly mentioning the context. Equipping robots with commonsense knowledge would lead to better communication between humans and robots and will allow robots... Read More about Commonsense-enhanced Natural Language Generation for Human-Robot Interaction.

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  .