Outputs (6)

The REAL Corpus: a crowd-sourced corpus of human generated and evaluated spatial references to real-world urban scenes (2016)
Presentation / Conference Contribution
Bartie, P., Mackaness, W., Gkatzia, D., & Rieser, V. (2016, May). The REAL Corpus: a crowd-sourced corpus of human generated and evaluated spatial references to real-world urban scenes. Presented at 10th International Conference on Language Resources and Evaluation (LREC)

We present a newly crowd-sourced data set of natural language references to objects anchored in complex urban scenes (In short: The REAL Corpus – Referring Expressions Anchored Language). The REAL corpus contains a collection of images of real-world... Read More about The REAL Corpus: a crowd-sourced corpus of human generated and evaluated spatial references to real-world urban scenes.

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). A Snapshot of NLG Evaluation Practices 2005 - 2014. . https://doi.org/10.18653/v1/w15-4708

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.

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.