Amalie Pauli


2023

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TeamAmpa at SemEval-2023 Task 3: Exploring Multilabel and Multilingual RoBERTa Models for Persuasion and Framing Detection
Amalie Pauli | Rafael Sarabia | Leon Derczynski | Ira Assent
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper describes our submission to theSemEval 2023 Task 3 on two subtasks: detectingpersuasion techniques and framing. Bothsubtasks are multi-label classification problems. We present a set of experiments, exploring howto get robust performance across languages usingpre-trained RoBERTa models. We test differentoversampling strategies, a strategy ofadding textual features from predictions obtainedwith related models, and present bothinconclusive and negative results. We achievea robust ranking across languages and subtaskswith our best ranking being nr. 1 for Subtask 3on Spanish.

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Sren Kierkegaard at SemEval-2023 Task 4: Label-aware text classification using Natural Language Inference
Ignacio Talavera Cepeda | Amalie Pauli | Ira Assent
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

In this paper, we describe our approach to Task 4 in SemEval 2023. Our pipeline tries to solve the problem of multi-label text classification of human values in English-written arguments. We propose a label-aware system where we reframe the multi-label task into a binary task resembling an NLI task. We propose to include the semantic description of the human values by comparing each description to each argument and ask whether there is entailment or not.

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Anchoring Fine-tuning of Sentence Transformer with Semantic Label Information for Efficient Truly Few-shot Classification
Amalie Pauli | Leon Derczynski | Ira Assent
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Few-shot classification is a powerful technique, but training requires substantial computing power and data. We propose an efficient method with small model sizes and less training data with only 2-8 training instances per class. Our proposed method, AncSetFit, targets low data scenarios by anchoring the task and label information through sentence embeddings in fine-tuning a Sentence Transformer model. It uses contrastive learning and a triplet loss to enforce training instances of a class to be closest to its own textual semantic label information in the embedding space - and thereby learning to embed different class instances more distinct. AncSetFit obtains strong performance in data-sparse scenarios compared to existing methods across SST-5, Emotion detection, and AG News data, even with just two examples per class.

2022

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Modelling Persuasion through Misuse of Rhetorical Appeals
Amalie Pauli | Leon Derczynski | Ira Assent
Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)

It is important to understand how people use words to persuade each other. This helps understand debate, and detect persuasive narratives in regard to e.g. misinformation. While computational modelling of some aspects of persuasion has received some attention, a way to unify and describe the overall phenomenon of when persuasion becomes undesired and problematic, is missing. In this paper, we attempt to address this by proposing a taxonomy of computational persuasion. Drawing upon existing research and resources, this paper shows how to re-frame and re-organise current work into a coherent framework targeting the misuse of rhetorical appeals. As a study to validate these re-framings, we then train and evaluate models of persuasion adapted to our taxonomy. Our results show an application of our taxonomy, and we are able to detecting misuse of rhetorical appeals, finding that these are more often used in misinformative contexts than in true ones.