All Outputs (54)

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.

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., Mahamood, S., Manning, E., Schoch, S., Thomson, C., & Wen, L. (2021, September). Underreporting of errors in NLG output, and what to do about it. Presented at 14th International Conference on Natural Language Generation, Aberdeen, UK

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.

The Task2Dial Dataset: A Novel Dataset for Commonsense-enhanced Task-based Dialogue Grounded in Documents (2021)
Presentation / Conference Contribution
Strathearn, C., & Gkatzia, D. (2021, November). The Task2Dial Dataset: A Novel Dataset for Commonsense-enhanced Task-based Dialogue Grounded in Documents. Presented at 4th International Conference on Natural Language and Speech Processing (ICNLSP 2021), Trento, Italy [Online]

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.

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, March). "What's this?" Comparing Active learning Strategies for Concept Acquisition in HRI. Presented at HRI'21: ACM/IEEE International Conference on Human-Robot Interaction, Online

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.

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.

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  .

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.

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.

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, September). Improving the Naturalness and Expressivity of Language Generation for Spanish. Presented at 10th International Conference on Natural Language Generation, Santiago de Compostela, Spain

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, 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.

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.