Papers by Luis Espinosa-Anke
Construction Artifacts in Metaphor Identification Datasets (2023.emnlp-main)
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| Challenge: | Existing metaphor identification datasets can be gamed by completely ignoring the potential metaphorical expression or the context in which it occurs. |
| Approach: | They show that existing metaphor identification datasets can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs. |
| Outcome: | The proposed system can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs. |
Grouping Entities with Shared Properties using Multi-Facet Prompting and Property Embeddings (2025.emnlp-main)
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| Challenge: | Methods for learning taxonomies from data are well-studied, but it is difficult to use them in large domains. |
| Approach: | They propose to use LLMs to describe the different properties that are satisfied by each entity individually and then use pre-trained embeddings to cluster these properties. |
| Outcome: | The proposed model can be used to describe the properties of the entities and group them into clusters. |
Improving Cross-Lingual Word Embeddings by Meeting in the Middle (D18-1)
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| Challenge: | Cross-lingual word embeddings are becoming increasingly important in multilingual NLP. |
| Approach: | They propose to apply an additional transformation after initial alignment to align two disjoint monolingual vector spaces. |
| Outcome: | The proposed approach outperforms state-of-the-art models in monolingual and cross-lingual evaluation tasks. |
Self-Supervised Intermediate Fine-Tuning of Biomedical Language Models for Interpreting Patient Case Descriptions (2022.coling-1)
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| Challenge: | Existing work has found that biomedical language models lack the knowledge needed for such tasks. |
| Approach: | They propose to fine-tune biomedical language models on the task of predicting masked medical concepts from PubMed abstracts to improve their performance. |
| Outcome: | The proposed strategy improves the performance of biomedical language models on the task of predicting masked medical concepts from patient case descriptions. |
Modelling Commonsense Properties Using Pre-Trained Bi-Encoders (2022.coling-1)
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| Challenge: | Pre-trained language models can capture commonsense properties that are rarely expressed in text. |
| Approach: | They propose to fine-tune language models to explicitly model commonsense properties . they train separate concept and property encoders on extracted hyponym-hypernym pairs and generic sentences . |
| Outcome: | The proposed model can capture commonsense properties with higher accuracy than human models . a new study shows that the model can model commonsensence properties with much higher accuracy . |
Interpretable Emoji Prediction via Label-Wise Attention LSTMs (D18-1)
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| Challenge: | Emojis are the evolution of characterbased emoticons and are used to express ideas about a myriad of topics. |
| Approach: | They propose a label-wise attention mechanism to better understand emoji prediction . they propose to model e-mails with eojis and then label them based on their meaning . |
| Outcome: | The proposed model improves over baselines and does particularly well when predicting infrequent emojis. |
Dialz: A Python Toolkit for Steering Vectors (2025.acl-demo)
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| Challenge: | *Dialz* is a Python library for advancing research on steering vectors for open-source LMs. |
| Approach: | They propose a Python library for advancing research on steering vectors for open-source LMs. |
| Outcome: | The proposed method reduces harmful outputs and provides insights into model behaviour across different layers. |
TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media (2022.coling-1)
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Daniel Loureiro, Aminette D’Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa-Anke, Leonardo Neves, Francesco Barbieri, Jose Camacho-Collados
| Challenge: | Language models are often clean and time-invariant, and do little to no account of social media usage. |
| Approach: | They propose a benchmark to accelerate research in social media-based meaning shift. |
| Outcome: | The proposed benchmark is aimed at accelerating research in social media-based meaning shift. |
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research (2023.findings-emnlp)
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Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri, Leonardo Neves, Kiamehr Rezaee, Luis Espinosa-Anke, Jiaxin Pei, Jose Camacho-Collados
| Challenge: | specialised language models (LMs) have shown to exhibit lower perplexity and higher downstream performance across the board. |
| Approach: | They propose a benchmark for NLP evaluation in social media, SuperTweetEval. |
| Outcome: | The proposed benchmark shows that social media models perform better when compared to general-purpose models, metrics and benchmarks. |
Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs (2026.findings-eacl)
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| Challenge: | Despite efforts to mitigate social bias in large language models, representational harms such as stereotyping continue to exist in both open and closed-source models. |
| Approach: | They propose a method to modify model activations in forward passes by applying steering vectors to a BBQ dataset and comparing their results to bias mitigation methods. |
| Outcome: | The proposed method outperforms 3 other bias mitigation methods on the BBQ dataset and shows the lowest impact on MMLU scores. |
Who is better at math, Jenny or Jingzhen? Uncovering Stereotypes in Large Language Models (2024.emnlp-main)
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| Challenge: | Existing research on stereotypes in large language models is limited and focuses on African Ameri- F. |
| Approach: | They propose to use global bias to probe a set of large language models via perplexity to determine how certain stereotypes are represented in the model's internal representations. |
| Outcome: | The proposed model amplifys harmful stereotypes and shows that the demographic groups associated with stereotypes remain consistent across model likelihoods and outputs. |
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies (2023.emnlp-main)
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| Challenge: | Existing work on decontextualised concept embeddings from language models has focused on capturing taxonomic structure in concepts. |
| Approach: | They propose a strategy for identifying what different concepts have in common with others and representing them in terms of their properties. |
| Outcome: | The proposed approach improves the performance of state-of-the-art models for a multi-label classification problem. |
Syntactically Aware Neural Architectures for Definition Extraction (N18-2)
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| Challenge: | Existing approaches to identify definitional knowledge in text corpora are based on Wikipedia-like definitions. |
| Approach: | They propose to combine Convolutional and Recurrent Neural Networks to train definitional knowledge in text corpora. |
| Outcome: | The proposed models can be applied to more noisy domain-specific corpora. |
SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors (C18-1)
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| Challenge: | Word embeddings use fixed-dimensional vectors to represent the meaning of words. |
| Approach: | They propose a pipeline for learning relation vectors based on word vector averaging and an ad hoc autoencoder. |
| Outcome: | The proposed pipeline can capture aspects of word meaning complementary to word embeddings. |