Papers by Zied Bouraoui
A Mixture-of-Experts Model for Learning Multi-Facet Entity Embeddings (2020.coling-main)
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| Challenge: | Existing methods for learning entity embeddings from text descriptions leave it to downstream applications to identify these different facets and to select the most relevant ones. |
| Approach: | They propose a model that instead learns several vectors for each entity, each of which captures a different aspect of the considered domain. |
| Outcome: | The proposed model learns several vectors for each entity, each of which intuitively captures a different aspect of the considered domain. |
CONTOR: Benchmarking Strategies for Completing Ontologies with Plausible Missing Rules (2024.findings-emnlp)
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| Challenge: | Existing evaluations focus on distinguishing held-out ontologies from randomly corrupted ones, which often makes the task unrealistically easy. |
| Approach: | They propose to use the common description logic syntax for encoding ontology rules to test their effectiveness on manually annotated hard negatives. |
| Outcome: | The proposed models are compared with existing models and have been evaluated on different ontologies. |
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. |
Credal Concept Bottleneck Models for Epistemic–Aleatoric Uncertainty Decomposition (2026.acl-long)
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| Challenge: | Existing models face challenges when dealing with uncertainty. |
| Approach: | They propose a framework that decomposes concept uncertainty by construction . epistemic uncertainty is positively associated with prediction errors, whereas aleatoric uncertainty closely tracks disagreement . |
| Outcome: | The proposed framework decomposes concept uncertainty by construction . epistemic uncertainty is positively associated with prediction errors, whereas aleatoric uncertainty closely tracks disagreement . |
Modeling Complex Semantics Relation with Contrastively Fine-Tuned Relational Encoders (2025.acl-long)
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| Challenge: | Existing methods for learning relational embeddings fail to capture nuanced representations and rich semantics. |
| Approach: | They propose different relational encoders designed to capture diverse relational aspects and semantic properties of entity pairs. |
| Outcome: | The proposed encoders capture diverse relational aspects and semantic properties of entity pairs. |
Unsupervised Learning of Distributional Relation Vectors (P18-1)
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| Challenge: | Existing word embedding models rely on co-occurrence statistics to learn vector representations of word meaning. |
| Approach: | They propose a method which directly learns relation vectors from co-occurrence statistics. |
| Outcome: | The proposed method is based on a variant of GloVe, which has an explicit connection between word vectors and PMI weighted co-occurrence vectors. |
Sentence Selection Strategies for Distilling Word Embeddings from BERT (2022.lrec-1)
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| Challenge: | Using language models to learn word embeddings is a key feature of transformer-based language models. |
| Approach: | They propose to use language models to learn high-quality word vectors from as few as 5 to 10 sentences with a careful selection strategy. |
| Outcome: | The proposed strategies can learn high-quality word vectors from as few as 5 to 10 sentences. |
Explanation Quality Assessment as Ranking with Listwise Rewards (2026.findings-acl)
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| Challenge: | a new approach to explanation quality assessment is to rank explanations by relative quality . standard reward objectives do not preserve graded distinctions well enough for policy optimization . |
| Approach: | They reformulate explanation quality assessment as a ranking problem instead of a generation problem . they train listwise and pairwise ranking models to preserve ordinal structure . |
| Outcome: | The proposed model outperforms regression on score separation and performance on listwise and pairwise models. |
Can Language Models Learn Embeddings of Propositional Logic Assertions? (2024.lrec-main)
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| Challenge: | Existing methods for automating reasoning can no longer be used for natural language tasks. |
| Approach: | They propose to use transformer-based language models to reason about knowledge expressed in natural language rather than using LMs to perform reasoning directly. |
| Outcome: | The proposed approach is feasible to some extent, but lacks robustness. |
There’s No Such Thing as Simple Reasoning for LLMs (2025.findings-acl)
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| Challenge: | Existing work has focused on relatively complex “many-hop” reasoning problems. |
| Approach: | They analyse the performance of fine-tuned LLMs on simple reasoning problems . they find the models remain highly brittle, being susceptible to seemingly innocent perturbations . |
| Outcome: | The proposed models fail on simple reasoning problems, but are highly brittle . they are susceptible to seemingly innocent perturbations, such as adding duplicates to the set of premises and shuffling the order in which the premises are presented. |
Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings (2024.findings-acl)
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| Challenge: | Concept embeddings are a useful and efficient mechanism for injecting commonsense knowledge into downstream tasks. |
| Approach: | They propose to model commonalities in concepts by capturing a more diverse range of commonsense properties. |
| Outcome: | The proposed model captures a more diverse range of commonsense properties and improves ontology completion and ultra-fine entity typing tasks. |
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. |
Relation Induction in Word Embeddings Revisited (C18-1)
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| Challenge: | Existing approaches to relation induction are based on vector translations, but they are often inadequate for knowledge base completion. |
| Approach: | They propose to use Gaussian to explicitly model the variability of translations and Bayesian linear regression to encode the assumption that there is a linear relationship between the vector representations of related words. |
| Outcome: | The proposed models are based on translations but use Gaussian to model the variability of translations and encode soft constraints on the source and target words that may be chosen. |
Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy (2023.findings-emnlp)
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| Challenge: | Ultra-fine entity typing is a task of inferring the semantic types from a large set of fine-grained candidates that apply to a given entity mention. |
| Approach: | They propose to use pre-trained label embeddings to cluster the labels into semantic domains and treat them as additional types. |
| Outcome: | The proposed method improves the performance of existing models with high quality embeddings. |
AMenDeD: Modelling Concepts by Aligning Mentions, Definitions and Decontextualised Embeddings (2024.lrec-main)
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| Challenge: | Contextualised Language Models (LMs) improve on word embeddings by encoding meaning of words in context. |
| Approach: | They propose to learn a unified embedding space in which all three types of representations can be integrated. |
| Outcome: | The proposed model outperforms existing approaches in ontology completion tasks. |