All Outputs (6)

Commonsense-enhanced Natural Language Generation for Human-Robot Interaction (2020)
Conference Proceeding
Gkatzia, D. (2020). Commonsense-enhanced Natural Language Generation for Human-Robot Interaction. In 2nd Workshop on Natural Language Generation for Human-Robot Interaction (HRI 2020)

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.

Second Workshop on Natural Language Generation for Human-Robot Interaction (2020)
Conference Proceeding
Buschmeier, H., Ellen Foster, M., & Gkatzia, D. (2020). Second Workshop on Natural Language Generation for Human-Robot Interaction. In HRI '20: Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (646-647). https://doi.org/10.1145/3371382.3374853

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.

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.

Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definition (2020)
Conference Proceeding
Howcroft, D., Belz, A., Clinciu, M., Gkatzia, D., Hasan, S. A., Mahamood, S., …Rieser, V. (2020). Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definition. In Proceedings of the 13th International Conference on Natural Language Generation (169-182)

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.

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  .

Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter (2020)
Journal Article
Pitropakis, N., Kokot, K., Gkatzia, D., Ludwiniak, R., Mylonas, A., & Kandias, M. (2020). Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter. Machine Learning and Knowledge Extraction, 2(3), 192-215. https://doi.org/10.3390/make2030011

The proliferation of social media platforms changed the way people interact online. However, engagement with social media comes with a price, the users’ privacy. Breaches of users’ privacy, such as the Cambridge Analytica scandal, can reveal how the... Read More about Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter.