Data-to-Text Generation Improves Decision-Making Under Uncertainty (2017)
Journal Article
Gkatzia, D., Lemon, O., & Rieser, V. (2017). Data-to-Text Generation Improves Decision-Making Under Uncertainty. IEEE Computational Intelligence Magazine, 12(3), 10-17. https://doi.org/10.1109/MCI.2017.2708998

Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. This article presents a comparison of different information presentations for uncertain data and, for the first time, measures their e... Read More about Data-to-Text Generation Improves Decision-Making Under Uncertainty.

How to Talk to Strangers: generating medical reports for first time users (2016)
Presentation / Conference Contribution
Gkatzia, D., Rieser, V., & Lemon, O. (2016, July). How to Talk to Strangers: generating medical reports for first time users. Presented at FUZZ-IEEE 2016

We propose a novel approach for handling first-time
users in the context of automatic report generation from timeseries
data in the health domain. Handling first-time users is
a common problem for Natural Language Generation (NLG)
and interactive... Read More about How to Talk to Strangers: generating medical reports for first time users.

Natural Language Generation enhances human decision-making with uncertain information. (2016)
Presentation / Conference Contribution
Gkatzia, D., Lemon, O., & Rieser, V. (2016, August). Natural Language Generation enhances human decision-making with uncertain information. Presented at 54th Annual Meeting of the Association for Computational Linguistics (ACL) Volume 2 (short papers)

Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. We present a comparison of different information presentations for uncertain data and, for the first time, measure their effects on hu... Read More about Natural Language Generation enhances human decision-making with uncertain information..

From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes (2015)
Presentation / Conference Contribution
Gkatzia, D., Rieser, V., Bartie, P., & Mackaness, W. (2015, September). From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes. Presented at 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon

Predicting the success of referring expressions (RE) is vital for real world applications such as navigation systems. Traditionally, research has focused on studying Referring Expression Generation (REG) in virtual, controlled environments. In this p... Read More about From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes.

A Game-Based Setup for Data Collection and Task-Based Evaluation of Uncertain Information Presentation (2015)
Presentation / Conference Contribution
Gkatzia, D., Cercas Curry, A., Rieser, V., & Lemon, O. (2015, September). A Game-Based Setup for Data Collection and Task-Based Evaluation of Uncertain Information Presentation. Presented at 15th European Workshop on Natural Language Generation (ENLG 2015), University of Brighton, Brighton, UK

Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores, such as probabilities. A concrete example of such data is weather data. We will demo a game-based setup for exploring the effectiveness of different ap... Read More about A Game-Based Setup for Data Collection and Task-Based Evaluation of Uncertain Information Presentation.

A Snapshot of NLG Evaluation Practices 2005 - 2014 (2015)
Presentation / Conference Contribution
Gkatzia, D., & Mahamood, S. (2015, September). A Snapshot of NLG Evaluation Practices 2005 - 2014. Presented at Proceedings of the 15th European Workshop on Natural Language Generation (ENLG), University of Brighton Brighton, UK

In this paper we present a snapshot of endto-end NLG system evaluations as presented in conference and journal papers1 over the last ten years in order to better understand the nature and type of evaluations that have been undertaken. We find that re... Read More about A Snapshot of NLG Evaluation Practices 2005 - 2014.

Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments (2015)
Presentation / Conference Contribution
Cercas Curry, A., Gkatzia, D., & Rieser, V. (2015, September). Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments. Presented at 15th European Workshop on Natural Language Generation (ENLG 2015), University of Brighton, Brighton, UK

Referring to landmarks has been identified to lead to improved navigation instructions. However, a previous corpus study suggests that human “wizards” also choose to refer to street names and generate user-centric instructions. In this paper, we cond... Read More about Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments.

Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data (2015)
Presentation / Conference Contribution
McGookin, D., Gkatzia, D., & Hastie, H. (2015, August). Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data. Presented at 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, Copenhagen, Denmark

Navigation when running is exploratory, characterised by both starting and ending in the same location, and iteratively foraging the environment to find areas with the most suitable running conditions. Runners do not wish to be explicitly directed, o... Read More about Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data.

Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data (2014)
Presentation / Conference Contribution
Gkatzia, D., Hastie, H., & Lemon, O. (2014, June). Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data. Presented at The 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore

We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label (ML)
classification problem, which takes as input ti... Read More about Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data.

Finding middle ground? Multi-objective Natural Language Generation from time-series data (2014)
Presentation / Conference Contribution
Gkatzia, D., Hastie, H., & Lemon, O. (2014, April). Finding middle ground? Multi-objective Natural Language Generation from time-series data. Presented at Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers, Gothenburg, Sweden

A Natural Language Generation (NLG) system is able to generate text from nonlinguistic data, ideally personalising the content to a user’s specific needs. In some cases, however, there are multiple stakeholders with their own individual goals, needs... Read More about Finding middle ground? Multi-objective Natural Language Generation from time-series data.

Generating Verbal Descriptions from Medical Sensor Data: A Corpus Study on User Preferences (2014)
Presentation / Conference Contribution
Gkatzia, D., Rieser, V., Mcsporran, A., Mcgowan, A., Mort, A., & Dewar, M. (2014, September). Generating Verbal Descriptions from Medical Sensor Data: A Corpus Study on User Preferences. Presented at BCS Health Informatics Scotland, Glasgow

Understanding and interpreting medical sensor data is an essential part of pre-hospital care in medical emergencies, but requires training and previous knowledge. In this paper, we describe ongoing work towards a medical decision support tool, which... Read More about Generating Verbal Descriptions from Medical Sensor Data: A Corpus Study on User Preferences.

Multi-adaptive Natural Language Generation using Principal Component Regression (2014)
Presentation / Conference Contribution
Gkatzia, D., Hastie, H., & Lemon, O. (2014, June). Multi-adaptive Natural Language Generation using Principal Component Regression. Presented at International Natural Language Generation Conference (INLG)

We present FeedbackGen, a system that uses a multi-adaptive approach to Natural Language Generation. With the term 'multi-adaptive', we refer to a system that is able to adapt its content to different user groups simultaneously, in our case adapting... Read More about Multi-adaptive Natural Language Generation using Principal Component Regression.