Papers by Enrica Troiano

8 papers
Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing (2024.findings-emnlp)

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Challenge: Psychological studies aim at explaining internal mechanisms of emotions, while computational studies simplify them into labels.
Approach: They propose to treat emotions as strategies to cope with salient situations . they introduce a task of coping identification and a corpus constructed via role-playing .
Outcome: The proposed method allows to investigate the link between emotions and behavior, which also emerges in language.
A Computational Exploration of Exaggeration (D18-1)

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Challenge: a new computational approach to exaggeration detection is needed for non-literal phenomena . a corpus of overstatements (or hyperboles) is used to detect exaggrements .
Approach: They propose a computational approach to detect exaggerated sentences using crowdsourcing data . they build a corpus containing overstatements and then evaluate models trained on HYPO .
Outcome: The proposed approach can detect exaggerated sentences using a crowdsourced dataset.
Appraisal Theories for Emotion Classification in Text (2020.coling-main)

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Challenge: Automatic emotion categorization is based on textual units assigned to an emotion from a predefined inventory, for instance following the basic emotion classes proposed by Paul Ekman (1999) or Plutchik (2001).
Approach: They propose to make automatic emotion categorization explicit by following theories of cognitive appraisal of events and show their potential for emotion classification when being encoded in classification models.
Outcome: The proposed models improve the classification of discrete emotion categories by using appraisal dimension assignments in event descriptions.
ScanEZ: Integrating Cognitive Models with Self-Supervised Learning for Spatiotemporal Scanpath Prediction (2025.acl-short)

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Challenge: ScanEZ framework provides a framework for predicting scanpaths during reading . masked modeling of eye movements and cognitive model simulations are used to kick-start training.
Approach: They propose a framework for self-supervised learning that models scanpaths using synthetic data and a 3-D gaze objective inspired bymasked language modeling.
Outcome: The proposed framework achieves state-of-the-art results on established datasets and is portable across different conditions.
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation (2020.coling-main)

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Challenge: MT is used to support human-to-human communication across languages, but it is unclear whether it can translate the non-propositional level of emotions.
Approach: They propose to use a re-ranking approach to change emotions to reverse this tendency . they find that emotions are toned down or amplified through linguistic changes .
Outcome: The proposed model can be used to change emotions, and it can be applied to other languages.
CLAUSE-ATLAS: A Corpus of Narrative Information to Scale up Computational Literary Analysis (2024.lrec-main)

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Challenge: XIX and XX century English novels annotated automatically contain 41,715 labeled clauses . a new approach to analyze novels based on clauses captures structural patterns within books, as well as qualitative differences between them.
Approach: They propose to use a corpus of XIX and XX century English novels annotated automatically to study stories as sequences of eventive, subjective and contextual information.
Outcome: The proposed method captures structural patterns within books, as well as qualitative differences between them.
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations (2022.lrec-1)

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Challenge: Emotion classification is often formulated as the task to categorize texts into a predefined set of emotion classes.
Approach: They propose that a classification setup for emotion analysis should be performed in an integrated manner, including the different semantic roles that participate in an emotion episode.
Outcome: The proposed method reveals patterns in the co-occurrence of people’s emotions in interaction.
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English (P19-1)

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Challenge: Existing studies on automatic recognition of emotions in text have achieved promising results, but there is a shortage of resources for non-English languages, with few exceptions, like Chinese.
Approach: They propose to use a crowdsourced German emotion corpus to build a corpus similar to the English ISEAR emotion dataset.
Outcome: The proposed model performs well in German and English, but lacks the resources for non-English languages.

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