Nina Dethlefs


2024

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BDA at SemEval-2024 Task 4: Detection of Persuasion in Memes Across Languages with Ensemble Learning and External Knowledge
Victoria Sherratt | Sedat Dogan | Ifeoluwa Wuraola | Lydia Bryan-smith | Oyinkansola Onwuchekwa | Nina Dethlefs
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

This paper outlines our multimodal ensemble learning system for identifying persuasion techniques in memes. We contribute an approach which utilises the novel inclusion of consistent named visual entities extracted using Google Vision’s API as an external knowledge source, joined to our multimodal ensemble via late fusion. As well as detailing our experiments in ensemble combinations, fusion methods and data augmentation, we explore the impact of including external data and summarise post-evaluation improvements to our architecture based on analysis of the task results.

2023

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Linguistic Pattern Analysis in the Climate Change-Related Tweets from UK and Nigeria
Ifeoluwa Wuraola | Nina Dethlefs | Daniel Marciniak
Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD)

To understand the global trends of human opinion on climate change in specific geographical areas, this research proposes a framework to analyse linguistic features and cultural differences in climate-related tweets. Our study combines transformer networks with linguistic feature analysis to address small dataset limitations and gain insights into cultural differences in tweets from the UK and Nigeria. Our study found that Nigerians use more leadership language and informal words in discussing climate change on Twitter compared to the UK, as these topics are treated as an issue of salience and urgency. In contrast, the UK’s discourse about climate change on Twitter is characterised by using more formal, logical, and longer words per sentence compared to Nigeria. Also, we confirm the geographical identifiability of tweets through a classification task using DistilBERT, which achieves 83% of accuracy.

2022

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RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification
Annika Marie Schoene | Nina Dethlefs | Sophia Ananiadou
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Several existing resources are available for sentiment analysis (SA) tasks that are used for learning sentiment specific embedding (SSE) representations. These resources are either large, common-sense knowledge graphs (KG) that cover a limited amount of polarities/emotions or they are smaller in size (e.g.: lexicons), which require costly human annotation and cover fine-grained emotions. Therefore using knowledge resources to learn SSE representations is either limited by the low coverage of polarities/emotions or the overall size of a resource. In this paper, we first introduce a new directed KG called ‘RELATE’, which is built to overcome both the issue of low coverage of emotions and the issue of scalability. RELATE is the first KG of its size to cover Ekman’s six basic emotions that are directed towards entities. It is based on linguistic rules to incorporate the benefit of semantics without relying on costly human annotation. The performance of ‘RELATE’ is evaluated by learning SSE representations using a Graph Convolutional Neural Network (GCN).

2021

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Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
Haizhou Li | Gina-Anne Levow | Zhou Yu | Chitralekha Gupta | Berrak Sisman | Siqi Cai | David Vandyke | Nina Dethlefs | Yan Wu | Junyi Jessy Li
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

2020

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Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Olivier Pietquin | Smaranda Muresan | Vivian Chen | Casey Kennington | David Vandyke | Nina Dethlefs | Koji Inoue | Erik Ekstedt | Stefan Ultes
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

2019

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Dilated LSTM with attention for Classification of Suicide Notes
Annika M Schoene | George Lacey | Alexander P Turner | Nina Dethlefs
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

In this paper we present a dilated LSTM with attention mechanism for document-level classification of suicide notes, last statements and depressed notes. We achieve an accuracy of 87.34% compared to competitive baselines of 80.35% (Logistic Model Tree) and 82.27% (Bi-directional LSTM with Attention). Furthermore, we provide an analysis of both the grammatical and thematic content of suicide notes, last statements and depressed notes. We find that the use of personal pronouns, cognitive processes and references to loved ones are most important. Finally, we show through visualisations of attention weights that the Dilated LSTM with attention is able to identify the same distinguishing features across documents as the linguistic analysis.

2016

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Automatic Identification of Suicide Notes from Linguistic and Sentiment Features
Annika Marie Schoene | Nina Dethlefs
Proceedings of the 10th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

2014

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Cluster-based Prediction of User Ratings for Stylistic Surface Realisation
Nina Dethlefs | Heriberto Cuayáhuitl | Helen Hastie | Verena Rieser | Oliver Lemon
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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The PARLANCE mobile application for interactive search in English and Mandarin
Helen Hastie | Marie-Aude Aufaure | Panos Alexopoulos | Hugues Bouchard | Catherine Breslin | Heriberto Cuayáhuitl | Nina Dethlefs | Milica Gašić | James Henderson | Oliver Lemon | Xingkun Liu | Peter Mika | Nesrine Ben Mustapha | Tim Potter | Verena Rieser | Blaise Thomson | Pirros Tsiakoulis | Yves Vanrompay | Boris Villazon-Terrazas | Majid Yazdani | Steve Young | Yanchao Yu
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

2013

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Demonstration of the PARLANCE system: a data-driven incremental, spoken dialogue system for interactive search
Helen Hastie | Marie-Aude Aufaure | Panos Alexopoulos | Heriberto Cuayáhuitl | Nina Dethlefs | Milica Gasic | James Henderson | Oliver Lemon | Xingkun Liu | Peter Mika | Nesrine Ben Mustapha | Verena Rieser | Blaise Thomson | Pirros Tsiakoulis | Yves Vanrompay
Proceedings of the SIGDIAL 2013 Conference

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Impact of ASR N-Best Information on Bayesian Dialogue Act Recognition
Heriberto Cuayáhuitl | Nina Dethlefs | Helen Hastie | Oliver Lemon
Proceedings of the SIGDIAL 2013 Conference

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Conditional Random Fields for Responsive Surface Realisation using Global Features
Nina Dethlefs | Helen Hastie | Heriberto Cuayáhuitl | Oliver Lemon
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Optimising Incremental Generation for Spoken Dialogue Systems: Reducing the Need for Fillers
Nina Dethlefs | Helen Hastie | Verena Rieser | Oliver Lemon
INLG 2012 Proceedings of the Seventh International Natural Language Generation Conference

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Dialogue Systems Using Online Learning: Beyond Empirical Methods
Heriberto Cuayáhuitl | Nina Dethlefs
NAACL-HLT Workshop on Future directions and needs in the Spoken Dialog Community: Tools and Data (SDCTD 2012)

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Incremental Spoken Dialogue Systems: Tools and Data
Helen Hastie | Oliver Lemon | Nina Dethlefs
NAACL-HLT Workshop on Future directions and needs in the Spoken Dialog Community: Tools and Data (SDCTD 2012)

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Comparing HMMs and Bayesian Networks for Surface Realisation
Nina Dethlefs | Heriberto Cuayáhuitl
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Optimising Incremental Dialogue Decisions Using Information Density for Interactive Systems
Nina Dethlefs | Helen Hastie | Verena Rieser | Oliver Lemon
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Hierarchical Dialogue Policy Learning using Flexible State Transitions and Linear Function Approximation
Heriberto Cuayáhuitl | Ivana Kruijff-Korbayová | Nina Dethlefs
Proceedings of COLING 2012: Demonstration Papers

2011

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Hierarchical Reinforcement Learning and Hidden Markov Models for Task-Oriented Natural Language Generation
Nina Dethlefs | Heriberto Cuayáhuitl
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Optimising Natural Language Generation Decision Making For Situated Dialogue
Nina Dethlefs | Heriberto Cuayáhuitl | Jette Viethen
Proceedings of the SIGDIAL 2011 Conference

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Combining Hierarchical Reinforcement Learning and Bayesian Networks for Natural Language Generation in Situated Dialogue
Nina Dethlefs | Heriberto Cuayáhuitl
Proceedings of the 13th European Workshop on Natural Language Generation

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The Bremen System for the GIVE-2.5 Challenge
Nina Dethlefs
Proceedings of the 13th European Workshop on Natural Language Generation

2010

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Hierarchical Reinforcement Learning for Adaptive Text Generation
Nina Dethlefs | Heriberto Cuayáhuitl
Proceedings of the 6th International Natural Language Generation Conference