Papers by Andrianos Michail
Information Representation Fairness in Long-Document Embeddings: The Peculiar Interaction of Positional and Language Bias (2026.findings-acl)
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| Challenge: | Existing studies show that embedding models exhibit systematic positional and language biases when documents are longer and consist of multiple segments. |
| Approach: | They propose a permutation-based evaluation framework to quantify embedding biases . they propose an inference-time attention calibration method that redistributes attention more evenly across document positions . |
| Outcome: | The proposed framework reduces the positional and language biases in embedding models . the proposed framework improves the discoverability of later segments . |
Similar, but why? A Toolkit for Explaining Text Similarity (2026.eacl-demo)
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| Challenge: | XPLAINSIM is a Python package that explains textual similarity in an easy-to-use way. |
| Approach: | They propose a Python package that unifies three approaches to explain text similarity . they demonstrate the value of the package through intuitive examples and empirical research . |
| Outcome: | XPLAINSIM is a Python package that unifies three approaches to explain text similarity . the authors show that the package is useful for explaining text similarities in a simple way . |
PARAPHRASUS: A Comprehensive Benchmark for Evaluating Paraphrase Detection Models (2025.coling-main)
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| Challenge: | prevailing notion of paraphrase is simplistic, offering only limited view of vast spectrum of paraphrasing phenomena. |
| Approach: | They propose a benchmarking tool for paraphrase detection that provides a fine-grained evaluation lens. |
| Outcome: | The proposed benchmark enables rapid calibration of models to specific strictness levels. |
ConLoan: A Contrastive Multilingual Dataset for Evaluating Loanwords (2025.acl-long)
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Sina Ahmadi, Micha David Hess, Elena Álvarez-Mellado, Alessia Battisti, Cui Ding, Anne Göhring, Yingqiang Gao, Zifan Jiang, Andrianos Michail, Peshmerge Morad, Joel Niklaus, Maria Christina Panagiotopoulou, Stefano Perrella, Juri Opitz, Anastassia Shaitarova, Rico Sennrich
| Challenge: | Lexical borrowing is a ubiquitous linguistic phenomenon influenced by geopolitical, societal, and technological factors. |
| Approach: | They propose a novel contrastive dataset comprising sentences with and without loanwords across 10 languages to examine how machine translation and language models process loanword . |
| Outcome: | The proposed dataset shows that state-of-the-art models prefer loanwords over native terms and exhibit varying performance across languages. |
Interpretable Text Embeddings and Text Similarity Explanation: A Survey (2025.emnlp-main)
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
| Approach: | They propose a framework for interpretable text embeddings and text similarity explanation . they characterize the main ideas, approaches, and trade-offs and discuss lessons learned . |
| Outcome: | The proposed methods are compared with existing models and compare them with existing ones. |
Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples (2025.findings-emnlp)
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| Challenge: | Cross-Lingual Semantic Discrimination (CLSD) is a lightweight evaluation task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. |
| Approach: | They propose a lightweight task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. |
| Outcome: | The proposed task requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. |
Cheap Character Noise for OCR-Robust Multilingual Embeddings (2025.findings-acl)
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| Challenge: | Optical character recognition (OCR) is a key component of the digitization of historical documents. |
| Approach: | They propose a method that fine-tunes existing multilingual models using noisy texts and a contrastive loss. |
| Outcome: | The proposed model improves on the training data of existing models using noisy texts and a contrastive loss. |
Sentence Smith: Controllable Edits for Evaluating Text Embeddings (2025.emnlp-main)
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| Challenge: | Controllable and transparent text generation has been a long-standing goal in NLP . but previous approaches were hindered by parsing and generation insufficiencies . |
| Approach: | They propose a framework for English that has three steps: 1. Parsing a sentence into a semantic graph. 2. Applying human-designed semantic manipulation rules. 3. Generating text from the manipulated graph. |
| Outcome: | The proposed framework for English is based on a neural network and parsers. |