Papers by Ekaterina Shutova
Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models (2025.findings-naacl)
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| Challenge: | Using large vision-language models to understand cultural contexts is a critical area of research. |
| Approach: | They conduct a thorough evaluation of multimodal models at different scales, focusing on their alignment with cultural values. |
| Outcome: | The proposed models show that they exhibit sensitivity to cultural values but their performance is highly context-dependent. |
Joint Modelling of Emotion and Abusive Language Detection (2020.acl-main)
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| Challenge: | Existing methods for abuse detection focus on linguistic properties of comments and online communities of users, disregarding the emotional state of the users and how this might affect their language. |
| Approach: | They propose to combine emotion and abusive language detection to create a multi-task learning framework that allows one task to inform the other. |
| Outcome: | The proposed model improves on the previous models, incorporating affective features into the learning framework. |
Modelling the interplay of metaphor and emotion through multitask learning (D19-1)
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| Challenge: | Existing research suggests that metaphorical phrases are more emotionally evocative than their literal counterparts. |
| Approach: | They propose a joint model of the relationship between metaphor and emotion within a computational framework by using hard and soft parameter sharing. |
| Outcome: | The proposed model advances the state of the art in both of these tasks. |
Scientific and Creative Analogies in Pretrained Language Models (2022.findings-emnlp)
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| Challenge: | Existing analogy datasets focus on a limited set of analogical relations with a high similarity of the two domains between which the analogy holds. |
| Approach: | They propose a dataset that encodes analogy in pretrained language models . they use a system that maps attributes and relational structures across dissimilar domains . |
| Outcome: | The proposed dataset shows that state-of-the-art models achieve low performance on analogy tasks . |
Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions (2021.eacl-main)
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| Challenge: | Populist rhetoric has risen across the political sphere in recent years, but computational approaches to it have been scarce. |
| Approach: | They propose a dataset of 6861 reddit comments annotated for populist attitudes and a set of multi-task learning models that leverage emotion and group identification as auxiliary tasks. |
| Outcome: | The proposed models leverage emotion and group identification as auxiliary tasks to model populist rhetoric tasks. |
How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning (2023.emnlp-main)
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| Challenge: | Multilingual language models can learn generalisations useful for other languages . yet, it remains unclear to what extent and under which conditions these models benefit from multilingual data and cross-lingual sharing. |
| Approach: | They propose a training data attribution method to retrieve training samples from multilingual data that are most influential for test predictions in a given language. |
| Outcome: | The proposed method exploits the ability to learn generalisations useful for other languages on zero-shot cross-lingual transfer for many languages. |
Best-of-L: Cross-Lingual Reward Modeling for Mathematical Reasoning (2026.findings-eacl)
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| Challenge: | Recent studies have focused on improving reasoning ability in English models, with multilingual models receiving comparatively little attention. |
| Approach: | They propose a framework that ranks candidate reasoning traces across languages rather than within a single language. |
| Outcome: | The proposed framework improves accuracy by up to 10 points in English compared to using reward modeling within a single language. |
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification (N19-1)
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| Challenge: | Current approaches to metaphor identification use restricted linguistic contexts, e.g. by only considering a verb’s arguments or the sentence containing a phrase. |
| Approach: | They propose to train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods. |
| Outcome: | The proposed classifiers obtained state-of-the-art results on the 2018 VU Amsterdam metaphor identification task without complex metaphor-specific features or deep neural architectures employed by other systems. |
Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation (2021.acl-long)
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| Challenge: | Existing methods for word sense disambiguation (WSD) lack large annotated datasets with sufficient coverage of words . performance of such methods lags behind fully-supervised methods . a meta-learning model is proposed to solve this problem . |
| Approach: | They propose a model of semantic memory for supervised word sense disambiguation using meta-learning. |
| Outcome: | The proposed model improves performance in few-shot WSD and produces meaning prototypes that capture similar senses of distinct words. |
Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models (2020.tacl-1)
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| Challenge: | Existing research has focused on applying semantic models to decode brain activity associated with the meaning of individual words. |
| Approach: | They evaluate a range of semantic models to capture metaphor processing in the brain . they found that compositional models and word embeddings capture differences in the processing of literal and metaphoric sentences . |
| Outcome: | The proposed models capture differences in the processing of literal and metaphoric sentences, providing support for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension. |
The language of prompting: What linguistic properties make a prompt successful? (2023.findings-emnlp)
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| Challenge: | Recent studies show that pretraining and instruction-tuned LLMs can achieve impressive performance on a multitude of tasks. |
| Approach: | They propose to use a standard for prompting research to better understand linguistic properties of LLMs. |
| Outcome: | The proposed standard would improve the performance of pre-trained and instruction-tuned LLMs on a multitude of tasks. |
Paper Bullets: Modeling Propaganda with the Help of Metaphor (2023.findings-eacl)
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| Challenge: | We hypothesize that it can be beneficial to model propaganda and metaphor together . we identify propaganda using loaded language and name-calling . |
| Approach: | They hypothesize that it can be beneficial to model propaganda and metaphor together . they use two datasets to identify propaganda techniques in news articles and memes . |
| Outcome: | The proposed model improves performance for the two most common propaganda techniques, especially loaded language and name-calling. |
What’s the Meaning of Superhuman Performance in Today’s NLU? (2023.acl-long)
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Simone Tedeschi, Johan Bos, Thierry Declerck, Jan Hajič, Daniel Hershcovich, Eduard Hovy, Alexander Koller, Simon Krek, Steven Schockaert, Rico Sennrich, Ekaterina Shutova, Roberto Navigli
| Challenge: | Recent research has focused on developing larger pretrained language models and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities. |
| Approach: | They propose to use benchmarks such as SuperGLUE and SQUAD to evaluate PLMs' abilities in language understanding, reasoning, and reading comprehension to assess their performance. |
| Outcome: | The proposed benchmarks have serious limitations affecting comparison between humans and PLMs and provide recommendations for fairer and more transparent benchmarks. |
Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models (P19-1)
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| Challenge: | Existing studies have shown that distributional semantic models can be used to decode fMRI patterns associated with specific aspects of semantic composition, such as the negation function. |
| Approach: | They apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs. |
| Outcome: | The proposed models show reduced decoding of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing. |
Ruddit: Norms of Offensiveness for English Reddit Comments (2021.acl-long)
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| Challenge: | Existing methods to detect offensive language have been limited by categorical labels . however, there are several challenges in the detection of such content . |
| Approach: | They analyze Reddit comments with fine-grained, real-valued offensiveness scores . they evaluate the ability of widely-used neural models to predict offensiveness . |
| Outcome: | The proposed method produces highly reliable offensiveness scores and can predict scores on reddit comments. |
Are LLMs classical or nonmonotonic reasoners? Lessons from generics (2024.acl-short)
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| Challenge: | Recent research on nonmonotonic reasoning has provided evidence of impressive performance and flexible adaptation to machine generated or human critique. |
| Approach: | They propose to use generics to explain why birds fly and exceptions such as penguins don't fly to maintain stable beliefs on truth conditions of generics. |
| Outcome: | The proposed task features generics, such as ‘Birds fly’, and exceptions, ‘Penguins don’t fly’. |
Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks (2024.findings-naacl)
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| Challenge: | Recent work has proposed explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning (SFT) on per-language subnetworks as a means of better guiding cross-lingual sharing. |
| Approach: | They propose to explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning on per-language subnetworks to better guide cross-lingual sharing. |
| Outcome: | The proposed approach can increase language specialization of subnetworks in favor of more cross-lingual sharing. |
Author Profiling for Abuse Detection (C18-1)
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| Challenge: | Existing methods for detecting abusive content rely on textual cues and lexical cue information. |
| Approach: | They propose a method that incorporates community-based profiling features of Twitter users to detect abusive content by using a dataset of 16k tweets. |
| Outcome: | The proposed approach outperforms the current state-of-the-art in abuse detection on a dataset of 16k tweets. |
Phonemes to the Rescue: Multilingual Tokenization Based on International Phonetic Alphabet (2026.acl-long)
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| Challenge: | Widely-used subword tokenization approaches favor high-resource languages and tokenizer-free methods yield longer sequences for scripts with a higher bytes-per-character ratio. |
| Approach: | They propose to use the International Phonetic Alphabet (IPA) as a language-agnostic input representation for multilingual tokenizers. |
| Outcome: | The proposed model improves tokenization quality and generalizes more effectively to unseen languages and scripts. |
Recent advances in neural metaphor processing: A linguistic, cognitive and social perspective (2021.naacl-main)
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| Challenge: | Metaphor processing systems have benefited from recent studies on the role of metaphor in communication and deep learning for natural language processing. |
| Approach: | They present a review of automated metaphor processing and discuss their results from downstream NLP tasks. |
| Outcome: | The proposed system is based on the findings of a systematic and comprehensive survey of metaphor processing systems published five years ago. |
Probing LLMs for Joint Encoding of Linguistic Categories (2023.findings-emnlp)
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Giulio Starace, Konstantinos Papakostas, Rochelle Choenni, Apostolos Panagiotopoulos, Matteo Rosati, Alina Leidinger, Ekaterina Shutova
| Challenge: | Existing research suggests that a linguistic hierarchy emerges in large language models . little is known about how encodings of different linguistic phenomena interact within the models - and to what extent processing of linguistically-related categories relies on the same, shared model representations. |
| Approach: | They propose a framework for testing the joint encoding of linguistic categories in large language models. |
| Outcome: | The proposed framework shows that the same patterns hold across languages in multilingual LLMs. |
CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension (2023.findings-eacl)
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| Challenge: | Existing frameworks for referring expression comprehension with commonsense knowledge are lacking in the field of multimodal referring . |
| Approach: | They propose a framework for commonsense knowledge Enhanced Transformers which integrates commonsensible knowledge into representations of objects in an image. |
| Outcome: | The proposed framework improves on the existing state of the art in referring expression comprehension with commonsense knowledge (CK-Transformer) it achieves 3.14% accuracy over the existing framework. |
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? (2021.emnlp-main)
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| Challenge: | Existing studies on "gender bias" and "racial bias" focus on stereotypical attributes of word representations . a new method to elicit stereotypical information is proposed to capture stereotypical traits in language models . |
| Approach: | They propose a method to elicit stereotypical information from pretrained language models . they use fine-tuning on news sources to study their emotional effects . |
| Outcome: | The proposed method can be used to analyze emotion and stereotype shifts due to linguistic experience using fine-tuning on news sources. |
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing (2022.acl-long)
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Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, Ekaterina Shutova
| Challenge: | Meta-learning can help overcome resource scarcity in cross-lingual NLP problems . pre-training of models requires large annotated training sets for the task at hand . |
| Approach: | They propose to use meta-learning to train a model to learn a parameter initialization that can adapt quickly to new languages. |
| Outcome: | The proposed model-agnostic meta-learning improves on language transfer and standard supervised learning baselines for unseen, typologically diverse, and low-resource languages in a few-shot learning setup. |
Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning (2025.findings-naacl)
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| Challenge: | Large language models have shown a remarkable ability to learn and perform complex tasks through in-context learning (ICL). |
| Approach: | They analyse two state-of-the-art models, Llama-3-8B and InternLM2-20B on abstract pattern recognition and NLP tasks. |
| Outcome: | The proposed model can perform up to 32% better than previous models on abstract pattern recognition and NLP tasks. |
K-hop neighbourhood regularization for few-shot learning on graphs: A case study of text classification (2023.eacl-main)
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| Challenge: | We show that few-sample word-document graphs can be used for improved learning in low-resource settings. |
| Approach: | They propose a method to utilize word-document graph properties for improved learning in low-resource settings by using a regularizer for heterogeneous graphs. |
| Outcome: | The proposed method outperforms a baseline TextGCN with 17% accuracy over eight languages while performing on par with the state-of-the-art models. |
The Pragmatics behind Politics: Modelling Metaphor, Framing and Emotion in Political Discourse (2020.findings-emnlp)
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| Challenge: | Existing computational models of political discourse do not incorporate metaphor and emotion in their functions. |
| Approach: | They propose to combine metaphor, emotion and political rhetoric to model political discourse . they show that they advance in three tasks: predicting political perspective of news articles, party affiliation of politicians and framing of policy issues. |
| Outcome: | The proposed models improve political discourse prediction, party affiliation and framing of policy issues. |
A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detection (2024.findings-naacl)
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| Challenge: | despite the performance of community models for malicious content detection, misinformation and hate speech continue to propagate on social media networks. |
| Approach: | They propose a new evaluation setup for community models for malicious content detection based on a few-shot subgraph sampling approach to test generalisation of models using local explorations of a larger graph. |
| Outcome: | The proposed evaluation setup outperforms existing models on real-world graphs on a training graph. |
Abusive Language Detection with Graph Convolutional Networks (N19-1)
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| Challenge: | Existing approaches to abusive language detection only capture shallow properties of online communities . a new approach captures both the structure of online community and linguistic behavior of users . |
| Approach: | They propose a graph convolutional network approach that captures the linguistic behavior of users . they propose to model homophily by embeddings for authors that encode the structure of their communities . |
| Outcome: | The proposed approach captures both the structure and linguistic behavior of users in online communities . authors show that the proposed approach significantly advances the current state of the art . |
Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability (2021.findings-emnlp)
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| Challenge: | Abuse on the Internet is an important societal problem of our time. |
| Approach: | They propose to use user and community information to enhance detection of abusive language . they propose to propose properties that an explainable method should aim to exhibit . |
| Outcome: | The proposed methods leverage user and community information to enhance detection of abusive language. |
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation (2020.findings-emnlp)
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| Challenge: | Existing methods for word sense disambiguation (WSD) are limited and require large datasets annotated with word senses. |
| Approach: | They propose a meta-learning framework for few-shot word sense disambiguation where the goal is to learn to disambiguate unseen words from only a few labeled instances. |
| Outcome: | The proposed framework is based on a large training dataset and a small number of examples. |
Multilingual and cross-lingual document classification: A meta-learning approach (2021.eacl-main)
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| Challenge: | Existing methods to document classification in low-resource languages are under-resourced . 6% of the world's languages are spoken, and many have inadequate resources . |
| Approach: | They propose a meta-learning approach to document classification in low-resource languages . they propose 'nuclear-shot' cross-lingual adaptation to previously unseen languages based on limited data . |
| Outcome: | The proposed method performs on-par on some languages while under-resourced in others. |
Metaphor Understanding Challenge Dataset for LLMs (2024.acl-long)
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| Challenge: | Metaphor understanding is an essential task for large language models (LLMs). |
| Approach: | They propose to evaluate the metaphor understanding capabilities of large language models (LLMs) the metaphor understanding challenge dataset provides over 10k paraphrases and 1.5k instances of inapt paraphrase. |
| Outcome: | The metaphor understanding challenge dataset evaluates the performance of large language models on a range of NLU tasks. |
NeuroAda: Activating Each Neuron’s Potential for Parameter-Efficient Fine-Tuning (2025.emnlp-main)
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| Challenge: | Existing methods for parameter-efficient fine-tuning are limited and require computational and memory resources. |
| Approach: | They propose a parameter-efficient fine-tuning method that enables fine-grained model finetunation while maintaining high memory efficiency. |
| Outcome: | The proposed method reduces CUDA memory usage by up to 60% while maintaining high performance. |