Papers by Aline Villavicencio
Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates (2026.acl-long)
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| Challenge: | Large language models underperform in languages absent or underrepresented in training data, creating barrier to equitable access for speakers worldwide. |
| Approach: | They propose a selective parameter update strategy that proactively preserves source knowledge by identifying critical parameters critical to maintaining source abilities. |
| Outcome: | Experiments in five typologically diverse languages show that SSU mitigates catastrophic forgetting. |
Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks (N18-2)
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| Challenge: | Semantic Verbal Fluency tests have been used in the diagnosis of certain clinical conditions, like Dementia. |
| Approach: | They investigate three similarity measures for automatically identifying switches in semantic chains: semantic similarity from a manually constructed resource, word association strength and semantic relatedness, both calculated from corpora. |
| Outcome: | The proposed classifiers outperform those that use a gold standard taxonomy for clinical conditions. |
Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information (2023.acl-long)
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| Challenge: | Existing evaluation methods for open-domain dialogues are difficult due to the one-to-many issue of the open- domain dialogues. |
| Approach: | They propose a learning-based automatic evaluation metric which can robustly evaluate open-domain dialogues by augmenting CVAEs with a Next Sentence Prediction objective and employing Mutual Information to model the semantic similarity of text in the latent space. |
| Outcome: | The proposed method can evaluate open-domain dialogues on two open- domain dialogue datasets. |
Probing for idiomaticity in vector space models (2021.eacl-main)
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| Challenge: | Contextualised word representation models are used to represent idiomaticity in language. |
| Approach: | They propose probing measures to assess if some of the expected linguistic properties of noun compounds are readily available in some standard and widely used representations. |
| Outcome: | The proposed models show that idiomaticity is not yet accurately represented by contextualised models. |
Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels (2021.acl-long)
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| Challenge: | Existing resources for idiomaticity annotation only include ratings at type level . idioms such as noun compounds have been considered a challenge for NLP . |
| Approach: | They present a dataset with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token levels. |
| Outcome: | The proposed dataset shows that human annotations are not capturing idiomaticity as human annotation models. |
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss (2024.findings-acl)
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| Challenge: | Accurately modeling idiomatic or non-compositional language has been a longstanding challenge in natural language processing (NLP). |
| Approach: | They propose an approach to model idiomaticity effectively using a triplet loss that incorporates the asymmetric contribution of components words to an idiomatic meaning by using adaptive contrastive learning and resampling miners. |
| Outcome: | The proposed model outperforms previous models significantly on a SemEval challenge and outperformed previous alternatives in many metrics. |
An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference (2024.findings-emnlp)
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| Challenge: | Cross-lingual vocabulary adaptation (CVA) methods have been proposed for adapting models to a target language . but effectiveness of these methods on increasing inference efficiency of generative large language models has not been explored. |
| Approach: | They propose to use cross-lingual vocabulary adaptation methods to adapt models to a target language to improve downstream performance. |
| Outcome: | The proposed methods significantly speed up models in four languages and four natural language understanding tasks. |
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing datasets are limited to providing the degree of idiomaticity of expressions along with the literal and, where applicable, (a single) non-literal interpretation of MWEs. |
| Approach: | They propose to use a dataset to test the effectiveness of a language model in generating representations of sentences containing idioms. |
| Outcome: | The proposed model performs reasonably well on the one-shot and few-shot scenarios, but there is scope for improvement in the zero-shot scenario. |
Rethinking the Idiomaticity Decomposability Hypothesis: Evidence from Distributional Learning (2026.acl-long)
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| Challenge: | Decomposability is thought to predict syntactic flexibility, but is not attributed to distributional experience. |
| Approach: | They propose a model-internal measure of decomposability and relate it to human ratings, syntactic flexibility, and predictability while tracking idiom learning during pretraining. |
| Outcome: | The proposed model-internal measure correlates weakly with human judgments and shows a small but consistent negative relationship with syntactic flexibility. |
Improving Tokenisation by Alternative Treatment of Spaces (2022.emnlp-main)
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| Challenge: | Subword tokenisation is a key initial step in processing natural language . it uses a number of different methods to tokenise text, including a stringsearching technique and a word-matching technique. |
| Approach: | They propose to use a vocabulary-based approach to tokenise text using a numerical ID and a mathematical function to manipulate it. |
| Outcome: | The method is based on a set of training data and learning from it to build a vocabulary and tokenise it at inference time using this vocabulary and learnt parameters. |
The brWaC Corpus: A New Open Resource for Brazilian Portuguese (L18-1)
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| Challenge: | a large corpus for Brazilian Portuguese is needed for NLP applications . the corpus is 2.7 billion tokens, and domain diversity is maximized . |
| Approach: | They propose to build a large Web corpus for Brazilian Portuguese with 2.7 billion tokens . they also propose an updated sentence-level approach for the strict removal of duplicated content . |
| Outcome: | The proposed corpus is based on a pipeline methodology and is available for querying and downloading. |
GraphRAG-Rad: Concept-Aware Radiology Report Generation via Latent Visual-Semantic Retrieval (2026.eacl-srw)
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| Challenge: | Existing encoder-decoder models suffer from hallucinations, generating plausible but incorrect medical findings. |
| Approach: | They propose a novel architecture that integrates biomedical knowledge through a latent visual-semantic retrieval approach. |
| Outcome: | The proposed architecture achieves competitive performance with strong results across multiple metrics. |
Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality (P18-2)
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| Challenge: | Recurrent neural tensor networks (RNNs) increase capacity by augmenting the size of the hidden layer, with significant increase in computational cost. |
| Approach: | They propose restricted recurrent neural tensor networks (r-RNTNs) which reserve distinct hidden layer weights for frequent vocabulary words while sharing a single set of weights . |
| Outcome: | The proposed model outperforms unrestricted RNTNs using only a small fraction of the parameters of unrestrained RNNNs. |
Rolling the DICE on Idiomaticity: How LLMs Fail to Grasp Context (2025.acl-long)
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| Challenge: | Existing models fail to resolve idiomaticity when it depends on contextual understanding . idiom frequency influences performance but does not guarantee accurate interpretation. |
| Approach: | They propose a novel contrastive dataset to assess whether large language models can effectively leverage context to disambiguate idiomatic meanings. |
| Outcome: | The proposed model performs better on sentences deemed more likely by the model . collocational frequency and sentence probability influence performance but not accuracy . |
From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errors (2025.emnlp-main)
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| Challenge: | Existing methods for error or uncertainty estimation rely on logits, entropy, sampling variance. |
| Approach: | They propose an input-only method for anticipating language model errors using token-level likelihood features inspired by surprisal and the Uniform Information Density hypothesis. |
| Outcome: | The proposed method outperforms baseline models and standard models on linguistic datasets. |