Papers by Sophie Wu
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages (2025.acl-long)
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Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin, Jan Philip Wahle, Terry Ruas, Meriem Beloucif, Christine de Kock, Nirmal Surange, Daniela Teodorescu, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Alham Fikri Aji, Felermino D. M. A. Ali, Ilseyar Alimova, Vladimir Araujo, Nikolay Babakov, Naomi Baes, Ana-Maria Bucur, Andiswa Bukula, Guanqun Cao, Rodrigo Tufiño, Rendi Chevi, Chiamaka Ijeoma Chukwuneke, Alexandra Ciobotaru, Daryna Dementieva, Murja Sani Gadanya, Robert Geislinger, Bela Gipp, Oumaima Hourrane, Oana Ignat, Falalu Ibrahim Lawan, Rooweither Mabuya, Rahmad Mahendra, Vukosi Marivate, Alexander Panchenko, Andrew Piper, Charles Henrique Porto Ferreira, Vitaly Protasov, Samuel Rutunda, Manish Shrivastava, Aura Cristina Udrea, Lilian Diana Awuor Wanzare, Sophie Wu, Florian Valentin Wunderlich, Hanif Muhammad Zhafran, Tianhui Zhang, Yi Zhou, Saif M. Mohammad
| Challenge: | Emotion recognition is an umbrella term for several NLP tasks, but most work on high-resource languages has focused on low-resourced languages. |
| Approach: | They propose to use emotion recognition to describe perceived emotions in 28 different languages and across several domains to identify and annotate the datasets. |
| Outcome: | The proposed datasets cover low-resource languages from Africa, Asia, Eastern Europe, and Latin America, with instances labeled by fluent speakers. |
Efficient Annotator Reliability Assessment and Sample Weighting for Knowledge-Based Misinformation Detection on Social Media (2025.findings-naacl)
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Owen Cook, Charlie Grimshaw, Ben Peng Wu, Sophie Dillon, Jack Hicks, Luke Jones, Thomas Smith, Matyas Szert, Xingyi Song
| Challenge: | Misinformation spreads rapidly on social media, confusing the truth and targeting potentially vulnerable people. |
| Approach: | They propose to use inter- and intra-annotator agreement to understand the reliability of each annotator and influence the training of large language models based on annotators reliability. |
| Outcome: | The proposed framework utilises inter- and intra-annotator agreement to understand the reliability of each annotator and influence the training of large language models based on annotators reliability. |
Homophone2Vec: Embedding Space Analysis for Empirical Evaluation of Phonological and Semantic Similarity (2024.acl-srw)
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| Challenge: | Existing studies have shown that homophones with different semantic/syntactic contexts are easier for children to memorize. |
| Approach: | They propose a method for empirically evaluating the relationship between phonological and semantic similarity of linguistic units using embedding spaces. |
| Outcome: | The proposed method shows that Chinese character homophones have a positive semantic relationship at varying levels of sound-sharing. |
Probing Narrative Morals: A New Character-Focused MFT Framework for Use with Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods to categorize moral foundations in storytelling are limited. |
| Approach: | They propose a character-centric method to quantify moral foundations in storytelling using large language models and a novel Moral Foundations Character Action Questionnaire to validate their approach against human annotations. |
| Outcome: | The proposed method validates against human annotations and then applies to 2,697 folktales from 55 countries. |
Confabulation: The Surprising Value of Large Language Model Hallucinations (2024.acl-long)
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| Challenge: | 'confabulations' are inherently problematic and AI research should eliminate this flaw, but confabulation is not a problem. |
| Approach: | They argue that measurable semantic characteristics of large language model (LLM) hallucinations mirror a human propensity to utilize increased narrativity as a cognitive resource for sense-making and communication. |
| Outcome: | The proposed study shows that measurable semantic characteristics of LLM confabulations mirror human propensity to utilize increased narrativity as a cognitive resource for sense-making and communication. |
The Language of Interoception: Examining Embodiment and Emotion Through a Corpus of Body Part Mentions (2025.findings-emnlp)
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| Challenge: | 5% to 10% of posts include body part mentions in English text . text containing BPMs tends to be more emotionally charged, even when the BPM is not used to describe a physical reaction to the emotion in the text. |
| Approach: | They create corpora of body part mentions in online English text with human annotations for the emotions of the person whose body part is mentioned. |
| Outcome: | The proposed study is the first to investigate the connection between emotion, embodiment, and everyday language in a large sample of natural language data. |