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Outputs (49)

Reproducing Human Evaluation of Meaning Preservation in Paraphrase Generation (2024)
Presentation / Conference Contribution
Watson, L. N., & Gkatzia, D. (2024, May). Reproducing Human Evaluation of Meaning Preservation in Paraphrase Generation. Presented at HumEval2024 at LREC-COLING 2024, Turin, Italy

Reproducibility is a cornerstone of scientific research, ensuring the reliability and generalisability of findings. The ReproNLP Shared Task on Reproducibility of Evaluations in NLP aims to assess the reproducibility of human evaluation studies. This... Read More about Reproducing Human Evaluation of Meaning Preservation in Paraphrase Generation.

TaskMaster: A Novel Cross-platform Task-based Spoken Dialogue System for Human-Robot Interaction (2023)
Presentation / Conference Contribution
Strathearn, C., Yu, Y., & Gkatzia, D. (2023). TaskMaster: A Novel Cross-platform Task-based Spoken Dialogue System for Human-Robot Interaction. In Proceedings of The Joint CUI and HRI Workshop at HRI 2023

The most effective way of communication between humans and robots is through natural language communication. However, there are many challenges to overcome before robots can effectively converse in order to collaborate and work together with humans.... Read More about TaskMaster: A Novel Cross-platform Task-based Spoken Dialogue System for Human-Robot Interaction.

Unveiling NLG Human-Evaluation Reproducibility: Lessons Learned and Key Insights from Participating in the ReproNLP Challenge (2023)
Presentation / Conference Contribution
Watson, L., & Gkatzia, D. (2023). Unveiling NLG Human-Evaluation Reproducibility: Lessons Learned and Key Insights from Participating in the ReproNLP Challenge. In Proceedings of the 3rd Workshop on Human Evaluation of NLP Systems (69-74)

Human evaluation is crucial for NLG systems as it provides a reliable assessment of the quality, effectiveness, and utility of generated language outputs. However, concerns about the reproducibility of such evaluations have emerged, casting doubt on... Read More about Unveiling NLG Human-Evaluation Reproducibility: Lessons Learned and Key Insights from Participating in the ReproNLP Challenge.

Working with troubles and failures in conversation between humans and robots: workshop report (2023)
Journal Article
Förster, F., Romeo, M., Holthaus, P., Wood, L. J., Dondrup, C., Fischer, J. E., …Kapetanios, E. (2023). Working with troubles and failures in conversation between humans and robots: workshop report. Frontiers in Robotics and AI, 10, Article 1202306. ht

This paper summarizes the structure and findings from the first Workshop on Troubles and Failures in Conversations between Humans and Robots. The workshop was organized to bring together a small, interdisciplinary group of researchers working on misc... Read More about Working with troubles and failures in conversation between humans and robots: workshop report.

enunlg: a Python library for reproducible neural data-to-text experimentation (2023)
Presentation / Conference Contribution
Howcroft, D. M., & Gkatzia, D. (2023). enunlg: a Python library for reproducible neural data-to-text experimentation. In Proceedings of the 16th International Natural Language Generation Conference: System Demonstrations (4-5)

Over the past decade, a variety of neural ar-chitectures for data-to-text generation (NLG) have been proposed. However, each system typically has its own approach to pre-and post-processing and other implementation details. Diversity in implementatio... Read More about enunlg: a Python library for reproducible neural data-to-text experimentation.

LOWRECORP: the Low-Resource NLG Corpus Building Challenge (2023)
Presentation / Conference Contribution
Chandu, K. R., Howcroft, D., Gkatzia, D., Chung, Y., Hou, Y., Emezue, C., …Adewumi, T. (2023). LOWRECORP: the Low-Resource NLG Corpus Building Challenge. In The 16th International Natural Language Generation Conference: Generation Challenges (1-9)

Most languages in the world do not have sufficient data available to develop neural-network-based natural language generation (NLG) systems. To alleviate this resource scarcity, we propose a novel challenge for the NLG community: low-resource languag... Read More about LOWRECORP: the Low-Resource NLG Corpus Building Challenge.

Edge NLP for Efficient Machine Translation in Low Connectivity Areas (2023)
Presentation / Conference Contribution
Watt, T., Chrysoulas, C., & Gkatzia, D. (2023, October). Edge NLP for Efficient Machine Translation in Low Connectivity Areas. Presented at IEEE 9th World Forum on Internet of Things: 2nd Workshop on Convergence of Edge Intelligence in IoT (EdgeAI-IoT 202

Machine translation (MT) usually requires connectivity and access to the cloud which is often limited in many parts of the world, including hard to reach rural areas. Edge natural language processing (NLP) aims to solve this problem by processing lan... Read More about Edge NLP for Efficient Machine Translation in Low Connectivity Areas.

Building a dual dataset of text-and image-grounded conversations and summarisation in Gàidhlig (Scottish Gaelic) (2023)
Presentation / Conference Contribution
Howcroft, D. M., Lamb, W., Groundwater, A., & Gkatzia, D. (2023, September). Building a dual dataset of text-and image-grounded conversations and summarisation in Gàidhlig (Scottish Gaelic). Presented at The 16th International Natural Language Generation

Gàidhlig (Scottish Gaelic; gd) is spoken by about 57k people in Scotland, but remains an under-resourced language with respect to natural language processing in general and natural language generation (NLG) in particular. To address this gap, we deve... Read More about Building a dual dataset of text-and image-grounded conversations and summarisation in Gàidhlig (Scottish Gaelic).

Responsible Design & Evaluation of a Conversational Agent for a National Careers Service (2023)
Presentation / Conference Contribution
Wilson, M., Cruickshank, P., Gkatzia, D., & Robertson, P. (in press). Responsible Design & Evaluation of a Conversational Agent for a National Careers Service.

This PhD project applies a research-through-design approach to the development of a conversational agent for a national career service for young people. This includes addressing practical, interactional and ethical aspects of the system. For each asp... Read More about Responsible Design & Evaluation of a Conversational Agent for a National Careers Service.

Barriers and enabling factors for error analysis in NLG research (2023)
Journal Article
Van Miltenburg, E., Clinciu, M., Dušek, O., Gkatzia, D., Inglis, S., Leppänen, L., …Wen, L. (2023). Barriers and enabling factors for error analysis in NLG research. Northern European Journal of Language Technology, 9(1), https://doi.org/10.3384/nejlt

Earlier research has shown that few studies in Natural Language Generation (NLG) evaluate their system outputs using an error analysis, despite known limitations of automatic evaluation metrics and human ratings. This position paper takes the stance... Read More about Barriers and enabling factors for error analysis in NLG research.

Most NLG is Low-Resource: here's what we can do about it (2022)
Presentation / Conference Contribution
Howcroft, D. M., & Gkatzia, D. (2022). Most NLG is Low-Resource: here's what we can do about it. In Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM) (336-350)

Many domains and tasks in natural language generation (NLG) are inherently 'low-resource', where training data, tools and linguistic analyses are scarce. This poses a particular challenge to researchers and system developers in the era of machine-lea... Read More about Most NLG is Low-Resource: here's what we can do about it.

A Commonsense-Enhanced Document-Grounded Conversational Agent: A Case Study on Task-Based Dialogue (2022)
Book Chapter
Strathearn, C., & Gkatzia, D. (2023). A Commonsense-Enhanced Document-Grounded Conversational Agent: A Case Study on Task-Based Dialogue. In M. Abbas (Ed.), Analysis and Application of Natural Language and Speech Processing (123-144). Cham: Springer. http

This paper argues that future dialogue systems must retrieve relevant information from multiple structured and unstructured data sources in order to generate natural and informative responses as well as exhibit commonsense capabilities and flexibilit... Read More about A Commonsense-Enhanced Document-Grounded Conversational Agent: A Case Study on Task-Based Dialogue.

Multi3Generation: Multi-task, Multilingual, Multi-Modal Language Generation (2022)
Presentation / Conference Contribution
Barreiro, A., de Souza, J. G., Gatt, A., Bhatt, M., Lloret, E., Erdem, A., …Alhasani, M. (2022, June). Multi3Generation: Multi-task, Multilingual, Multi-Modal Language Generation. Poster presented at 23rd Annual Conference of the European Association fo

This paper presents the Multitask, Multilingual, Multimodal Language Generation COST Action – Multi3Generation (CA18231), an interdisciplinary network of research groups working on different aspects of language generation. This "metapaper" will serve... Read More about Multi3Generation: Multi-task, Multilingual, Multi-Modal Language Generation.

Opportunities and risks in the use of AI in career development practice (2022)
Journal Article
Wilson, M., Robertson, P., Cruickshank, P., & Gkatzia, D. (2022). Opportunities and risks in the use of AI in career development practice. Journal of the National Institute for Career Education and Counselling, 48(1), 48-57. https://doi.org/10.20856/jnice

The Covid-19 pandemic required many aspects of life to move online. This accelerated a broader trend for increasing use of ICT and AI, with implications for both the world of work and career development. This article explores the potential benefits a... Read More about Opportunities and risks in the use of AI in career development practice.

Chefbot: A Novel Framework for the Generation of Commonsense-enhanced Responses for Task-based Dialogue Systems (2021)
Presentation / Conference Contribution
Strathearn, C., & Gkatzia, D. (2021). Chefbot: A Novel Framework for the Generation of Commonsense-enhanced Responses for Task-based Dialogue Systems. In Proceedings of the 14th International Conference on Natural Language Generation (46-47)

Conversational systems aim to generate responses that are accurate, relevant and engaging, either through utilising neural end-to-end models or through slot filling. Human-to-human conversations are enhanced by not only the latest utterance of the in... Read More about Chefbot: A Novel Framework for the Generation of Commonsense-enhanced Responses for Task-based Dialogue Systems.

The Task2Dial Dataset: A Novel Dataset for Commonsense-enhanced Task-based Dialogue Grounded in Documents (2021)
Presentation / Conference Contribution
Strathearn, C., & Gkatzia, D. (2021). The Task2Dial Dataset: A Novel Dataset for Commonsense-enhanced Task-based Dialogue Grounded in Documents. In Proceedings of The Fourth International Conference on Natural Language and Speech Processing (ICNLSP 2021)

This paper describes the Task2Dial dataset, a novel dataset of document-grounded task-based dialogues in the food preparation domain , where an Information Giver (IG) provides instructions to an Information Follower (IF) so that the latter can succes... Read More about The Task2Dial Dataset: A Novel Dataset for Commonsense-enhanced Task-based Dialogue Grounded in Documents.

Underreporting of errors in NLG output, and what to do about it (2021)
Presentation / Conference Contribution
van Miltenburg, E., Clinciu, M., Dušek, O., Gkatzia, D., Inglis, S., Leppänen, L., …Wen, L. (2021). Underreporting of errors in NLG output, and what to do about it. In Proceedings of the 14th International Conference on Natural Language Generation (1

We observe a severe under-reporting of the different kinds of errors that Natural Language Generation systems make. This is a problem, because mistakes are an important indicator of where systems should still be improved. If authors only report overa... Read More about Underreporting of errors in NLG output, and what to do about it.

CAPE: Context-Aware Private Embeddings for Private Language Learning (2021)
Presentation / Conference Contribution
Plant, R., Gkatzia, D., & Giuffrida, V. (2021). CAPE: Context-Aware Private Embeddings for Private Language Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (7970-7978)

Neural language models have contributed to state-of-the-art results in a number of downstream applications including sentiment analysis, intent classification and others. However, obtaining text representations or embeddings using these models risks... Read More about CAPE: Context-Aware Private Embeddings for Private Language Learning.

The Task2Dial Dataset (2021)
Data
Gkatzia, D., & Strathearn, C. (2021). The Task2Dial Dataset. [Dataset]

URL: https://huggingface.co/datasets/cstrathe435/Task2Dial

It's Common Sense, isn't it? Demystifying Human Evaluations in Commonsense-enhanced NLG systems (2021)
Presentation / Conference Contribution
Mahamood, S., Clinciu, M., & Gkatzia, D. (2021). It's Common Sense, isn't it? Demystifying Human Evaluations in Commonsense-enhanced NLG systems. In Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)

Common sense is an integral part of human cognition which allows us to make sound decisions , communicate effectively with others and interpret situations and utterances. Endowing AI systems with commonsense knowledge capabilities will help us get cl... Read More about It's Common Sense, isn't it? Demystifying Human Evaluations in Commonsense-enhanced NLG systems.

"What's this?" Comparing Active learning Strategies for Concept Acquisition in HRI (2021)
Presentation / Conference Contribution
Belvedere, F., & Gkatzia, D. (2021). "What's this?" Comparing Active learning Strategies for Concept Acquisition in HRI. In HRI '21 Companion: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (205-209). https://doi.org/1

Social robotics aim to equip robots with the ability to exhibit socially intelligent behaviour while interacting in a face-to-face context with human partners. An important aspect of face-to-face social interaction includes the efficient recognition... Read More about "What's this?" Comparing Active learning Strategies for Concept Acquisition in HRI.

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 Definiti

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  .

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.

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.

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), Dubl

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.

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/make203

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.

Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction (2018)
Presentation / Conference Contribution
Belakova, J., & Gkatzia, D. (2018). Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction. In Proceedings of the Workshop on NLG for Human–Robot Interaction (8-11)

One of the most natural ways for human robot communication is through spoken language. Training human-robot interaction systems require access to large datasets which are expensive to obtain and labour intensive. In this paper, we des... Read More about Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction.

Proceedings of the Workshop on NLG for Human–Robot Interaction (2018)
Presentation / Conference Contribution
(2018). Proceedings of the Workshop on NLG for Human–Robot Interaction. In M. Ellen Foster, H. Buschmeier, & D. Gkatzia (Eds.),

Ellen Foster, M., H. Buschmeier, & D. Gkatzia (Eds.) (2018). Proceedings of the Workshop on NLG for Human–Robot Interaction.

Improving the Naturalness and Expressivity of Language Generation for Spanish (2017)
Presentation / Conference Contribution
Barros, C., Gkatzia, D., & Lloret, E. (2017). Improving the Naturalness and Expressivity of Language Generation for Spanish. In Proceedings of the 10th International Conference on Natural Language Generation (41-50). https://doi.org/10.18653/v1/W17-3505

We present a flexible Natural Language Generation approach for Spanish, focused on the surface realisation stage, which integrates an inflection module in order to improve the naturalness and expressivity of the generated language. This inflection mo... Read More about Improving the Naturalness and Expressivity of Language Generation for Spanish.

Inflection Generation for Spanish Verbs using Supervised Learning (2017)
Presentation / Conference Contribution
Barros, C., Gkatzia, D., & Lloret, E. (2017). Inflection Generation for Spanish Verbs using Supervised Learning. In Proceedings of the First Workshop on Subword and Character Level Models in NLP (136-141). https://doi.org/10.18653/v1/W17-4120

We present a novel supervised approach to inflection generation for verbs in Spanish. Our system takes as input the verb’s lemma form and the desired features such as person, number, tense, and is able to predict the appropriate grammatical conjugati... Read More about Inflection Generation for Spanish Verbs using Supervised Learning.

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.

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). The REAL Corpus: a crowd-sourced corpus of human generated and evaluated spatial references to real-world urban scenes. In 10th International Conference on Language Resources and Evaluation (LR

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.

How to Talk to Strangers: generating medical reports for first time users (2016)
Presentation / Conference Contribution
Gkatzia, D., Rieser, V., & Lemon, O. (2016). How to Talk to Strangers: generating medical reports for first time users. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). https://doi.org/10.1109/FUZZ-IEEE.2016.7737739

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.

The REAL corpus (2016)
Data
Bartie, P., Mackaness, W., Gkatzia, D., & Rieser, V. (2016). The REAL corpus. [Dataset]

Our interest is in people’s capacity to efficiently and effectively describe geographic objects in urban scenes. The broader ambition is to develop spatial models capable of equivalent functionality able to construct such referring expressions. To th... Read More about The REAL corpus.

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). From the Virtual to the RealWorld: Referring to Objects in Real-World Spatial Scenes. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (1936-1942). http

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.

Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments (2015)
Presentation / Conference Contribution
Cercas Curry, A., Gkatzia, D., & Rieser, V. (2015). Generating and Evaluating Landmark-Based Navigation Instructions in Virtual Environments. In Proceedings of the 15th European Workshop on Natural Language Generation (90-94). https://doi.org/10.18653/v1

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.

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.

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). A Game-Based Setup for Data Collection and Task-Based Evaluation of Uncertain Information Presentation. In Proceedings of the 15th European Workshop on Natural Language Generation (112-113).

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.

Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data (2015)
Presentation / Conference Contribution
McGookin, D., Gkatzia, D., & Hastie, H. (2015). Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and S

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.

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

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.

Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data (2014)
Presentation / Conference Contribution
Gkatzia, D., Hastie, H., & Lemon, O. (2014). Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data. In Proceedings of the Conference Volume 1: Long Papers (1231-1240). https://doi.org/10.3115/v1/p14-1116

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.

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). Generating Verbal Descriptions from Medical Sensor Data: A Corpus Study on User Preferences. In BCS Health Informatics Scotland (HIS)

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). Multi-adaptive Natural Language Generation using Principal Component Regression. In Proceedings of the 8th International Natural Language Generation Conference (138-142)

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.

Generating student feedback from time-series data using Reinforcement Learning (2013)
Presentation / Conference Contribution
Gkatzia, D., Hastie, H., Janarthanam, S., & Lemon, O. (2013). Generating student feedback from time-series data using Reinforcement Learning. In Proceedings of the 14th European Workshop on Natural Language Generation (115-124)

We describe a statistical Natural LanguageGeneration (NLG) method for summarisa-tion of time-series data in the context offeedback generation for students. In thispaper, we initially present a method forcollecting time-series data f... Read More about Generating student feedback from time-series data using Reinforcement Learning.