Papers by Ion Androutsopoulos
Neural Legal Judgment Prediction in English (P19-1)
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| Challenge: | Recent work on legal judgment prediction has focused on Chinese, but only feature-based models have been considered in English. |
| Approach: | They propose a hierarchical version of BERT which bypasses BERT’s length limitation. |
| Outcome: | The proposed model outperforms existing models in binary violation classification, multi-label classification and case importance prediction. |
LEGAL-BERT: The Muppets straight out of Law School (2020.findings-emnlp)
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| Challenge: | Existing guidelines for pre-training and fine-tuning do not always generalize well in the legal domain. |
| Approach: | They propose to use BERT out of the box, adapt it by additional pre-training on domain-specific corpora, and pre-train it from scratch on domains. |
| Outcome: | The proposed strategies are: use the original BERT out of the box, adapt it by additional pre-training on domain-specific corpora, and pre-train it from scratch on domain specific corpors. |
SUM-QE: a BERT-based Summary Quality Estimation Model (D19-1)
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| Challenge: | SUM-QE is a quality estimation model for summarization that captures linguistic qualities that traditional evaluation metrics fail to capture. |
| Approach: | They propose a new quality estimation model based on BERT that addresses linguistic quality aspects that are only indirectly captured by content-based approaches to summary evaluation without comparison with human ratings. |
| Outcome: | The proposed model outperforms existing models on linguistic quality aspects that are only indirectly captured by content-based summarization evaluations without comparison with human ratings. |
Obligation and Prohibition Extraction Using Hierarchical RNNs (P18-2)
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| Challenge: | Existing methods for contract element extraction and contract element classification focus on indicative tokens, but they are not as efficient as the current ones. |
| Approach: | They propose a self-attention mechanism that converts each sentence to an embedding and processes the embeddables to classify each sentence. |
| Outcome: | The proposed method outperforms the flat BILSTM classifier even when it considers surrounding sentences because it has a broader discourse view. |
Should I try multiple optimizers when fine-tuning a pre-trained Transformer for NLP tasks? Should I tune their hyperparameters? (2024.eacl-long)
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| Challenge: | NLP research has explored different neural model architectures and sizes, datasets, training objectives, and transfer-learning techniques. |
| Approach: | They propose to use a variant of Stochastic Gradient Descent (SGD) to select among numerous variants, often with minimal or no tuning of the optimizer’s hyperparameters. |
| Outcome: | Experiments with five GLUE datasets, two models and seven popular optimizers show that tuning just the learning rate is as good as tuning all the hyperparameters. |
MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer (2021.emnlp-main)
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| Challenge: | MULTI-EURLEX is a dataset for topic classification of EU legal documents . fine-tuning a multilingually pretrained model in a single source language leads to catastrophic forgetting of multilingual knowledge and poor zero-shot transfer to other languages. |
| Approach: | They propose to use the dataset as a testbed for zero-shot cross-lingual transfer to exploit annotated training documents in one language to classify documents in another language. |
| Outcome: | The proposed model can be used to classify EU legal documents in other languages without a single source language and retain multilingual knowledge. |
Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases (2021.naacl-main)
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Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, Nikolaos Aletras, Ion Androutsopoulos, Prodromos Malakasiotis
| Challenge: | Interpretability or explainability is an emerging field of research in NLP . experimental results indicate that the newly introduced task is very challenging . |
| Approach: | They propose to extract rationales as paragraphs in multi-paragraph structured court cases . they also propose a constraint that allows models to be more specific . |
| Outcome: | The proposed task is very challenging and there is a large scope for further research. |
SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression (N19-1)
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| Challenge: | Neural sequence-to-sequence models are currently the dominant approach in natural language processing tasks, but require massive parallel corpora. |
| Approach: | They propose a sequence-to-sequence-tosequnce autoencoder with words as latent variables . they apply the model to unsupervised abstractive sentence compression . |
| Outcome: | The proposed model achieves promising results in unsupervised sentence compression on benchmark datasets. |
FiNER: Financial Numeric Entity Recognition for XBRL Tagging (2022.acl-long)
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Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, Georgios Paliouras
| Challenge: | Publicly traded companies are required to submit periodic reports with eXtensive Business Reporting Language (XBRL) word-level tags. |
| Approach: | They propose to use XBRL tagging as a new entity extraction task for the financial domain and release FiNER-139, a dataset of 1.1M sentences with gold X brl tags. |
| Outcome: | The proposed solution replaces numeric expressions with pseudo-tokens reflecting original token shapes and numeric magnitudes. |
Large-Scale Multi-Label Text Classification on EU Legislation (P19-1)
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| Challenge: | Large-Scale multi-label text classification is a task of assigning to each document all the relevant labels from a large set, typically containing thousands of labels (classes). |
| Approach: | They propose to use a dataset of 57k English EU legislative documents annotated with 4.3k EUROVOC labels for LMTC, few-shot learning and contextual embeddings. |
| Outcome: | The proposed dataset is suitable for LMTC, few- and zero-shot learning and bypasses the maximum text length limit. |
PolyNarrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News Articles (2025.acl-long)
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Nikolaos Nikolaidis, Nicolas Stefanovitch, Purificação Silvano, Dimitar Iliyanov Dimitrov, Roman Yangarber, Nuno Guimarães, Elisa Sartori, Ion Androutsopoulos, Preslav Nakov, Giovanni Da San Martino, Jakub Piskorski
| Challenge: | a new dataset of news articles annotated for narratives provides a framework for narrative detection . recurring narratives can propagate with very high velocity across audiences, languages and countries . |
| Approach: | They propose a multilingual dataset annotated for narratives using two-level taxonomies . they define narrative as a recurring, repetitive, overt or implicit claim that promotes a specific interpretation or viewpoint on an ongoing topic . |
| Outcome: | The proposed dataset will foster research in narrative detection and enable new research directions . the authors identify multiple narratives in the same article, and the results are published online . |
Deep Relevance Ranking Using Enhanced Document-Query Interactions (D18-1)
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| Challenge: | Document relevance ranking is the task of ranking documents from a large collection using the query and the text of each document only. |
| Approach: | They propose to use convolutional n-gram matching to inject rich context-sensitive encodings into their models, inspired by PACRR's convolution-based ngram matching features. |
| Outcome: | The proposed models outperform baselines, DRMM, and PACRR on the BIOASQ and TREC ROBUST questions and document inputs. |
A Data-Driven Guided Decoding Mechanism for Diagnostic Captioning (2024.findings-acl)
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Panagiotis Kaliosis, John Pavlopoulos, Foivos Charalampakos, Georgios Moschovis, Ion Androutsopoulos
| Challenge: | Diagnostic Captioning (DC) systems receive one or more medical images of a patient, such as X-Rays or Magnetic Resonance Images (MRIs). |
| Approach: | They propose a data-driven guided decoding method that incorporates medical information into the beam search of the diagnostic text generation process. |
| Outcome: | The proposed method improves on two medical datasets and can be used in few- and zero-shot learning scenarios. |
BioRead: A New Dataset for Biomedical Reading Comprehension (L18-1)
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| Challenge: | BioRead is a publicly available cloze-style biomedical machine reading comprehension (MRC) dataset with 16.4 million passage-question instances. |
| Approach: | They propose to build a cloze-style biomedical machine reading comprehension (MRC) dataset with 16.4 million passage-question instances. |
| Outcome: | The proposed method outperforms baselines on bioReadLite and bioASQ, and is currently the best on BioReadLite. |
A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections (2021.acl-long)
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| Challenge: | Question answering systems typically use pipelines that retrieve documents at finer text granularities. |
| Approach: | They propose an architecture for document and snippet ranking that leverages intuition . they modified a natural questions dataset to test their model . |
| Outcome: | The proposed model outperforms pipelines in document retrieval on biomedical data . the proposed model is competitive with the existing model, despite fewer parameters . |
An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels (2020.emnlp-main)
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Ilias Chalkidis, Manos Fergadiotis, Sotiris Kotitsas, Prodromos Malakasiotis, Nikolaos Aletras, Ion Androutsopoulos
| Challenge: | Large-scale Multi-label Text Classification (LMTC) is a type of classification that assigns labels to a large set of labels. |
| Approach: | They propose to use probabilistic label trees to improve frequent, few and zero-shot learning . they propose to combine a new state-of-the-art method with pre-trained Transformers . |
| Outcome: | The proposed models outperform existing models on frequent, few and zero-shot learning on three datasets from different domains. |
Toxicity Detection: Does Context Really Matter? (2020.acl-main)
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| Challenge: | Existing ‘toxicity’ detection datasets and models ignore the context of the posts, implicitly assuming that comments may be judged independently. |
| Approach: | They limit the notion of context to the previous post in the thread and the discussion title and focus on how it affects human judgement. |
| Outcome: | The proposed model can amplify or mitigate perceived toxicity of posts and a small but significant subset of manually labeled posts end up having the opposite toxicity labels if the annotators are not provided with context. |
Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey (2025.emnlp-main)
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Katerina Korre, Dimitris Tsirmpas, Nikos Gkoumas, Emma Cabalé, Danai Myrtzani, Theodoros Evgeniou, Ion Androutsopoulos, John Pavlopoulos
| Challenge: | Recent advances in LLMs enable artificial facilitation agents to not only moderate content, but also actively improve the quality of interactions. |
| Approach: | They propose a taxonomy on discussion quality evaluation and a new taxonomies for intervention and facilitation strategies. |
| Outcome: | The proposed methods synthesize ideas from Natural Language Processing (NLP) and Social Sciences to provide a taxonomy on discussion quality evaluation, and a roadmap of good practices and future research directions. |
GreekBarBench: A Challenging Benchmark for Free-Text Legal Reasoning and Citations (2025.findings-emnlp)
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| Challenge: | GreekBarBench evaluates LLMs on legal questions across five different legal areas from the Greek Bar exams. |
| Approach: | They propose a three-dimensional scoring system and an LLM-as-a-judge approach to tackle the challenges of free-text evaluation. |
| Outcome: | The proposed system uses an LLM-as-a-judge approach to evaluate LLMs on legal questions across five legal areas from the Greek Bar exams. |
GR-NLP-TOOLKIT: An Open-Source NLP Toolkit for Modern Greek (2025.coling-demos)
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Lefteris Loukas, Nikolaos Smyrnioudis, Chrysa Dikonomaki, Spiros Barbakos, Anastasios Toumazatos, John Koutsikakis, Manolis Kyriakakis, Mary Georgiou, Stavros Vassos, John Pavlopoulos, Ion Androutsopoulos
| Challenge: | GR-NLP-TOOLKIT is an open-source natural language processing toolkit for modern Greek. |
| Approach: | They present GR-NLP-TOOLKIT, an open-source natural language processing toolkit for Greek. |
| Outcome: | The toolkit provides state-of-the-art performance in five core NLP tasks . it can be easily installed in Python and is accessible through a demonstration platform on HuggingFace . |
From the Detection of Toxic Spans in Online Discussions to the Analysis of Toxic-to-Civil Transfer (2022.acl-long)
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| Challenge: | a dataset of English posts with annotations of toxic spans is released . sequence labeling models perform best, but rationale extraction methods are promising . |
| Approach: | They propose a dataset for toxic spans detection that includes an annotation of toxic posts . they propose to add generic rationale extraction mechanisms to the model to obtain toxic span information . |
| Outcome: | The proposed framework is based on a dataset of English posts with toxic span annotations . it shows that sequence labeling models perform best, but that rationale extraction methods are promising . |
Cache me if you Can: an Online Cost-aware Teacher-Student framework to Reduce the Calls to Large Language Models (2023.findings-emnlp)
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| Challenge: | Prompting Large Language Models (LLMs) performs impressively in zero- and few-shot settings. |
| Approach: | They propose a framework that allows reducing calls to LLMs by caching previous LLM responses and using them to train a local inexpensive model on the SME side. |
| Outcome: | The proposed framework reduces calls to LLMs by caching previous LLM responses and using them to train a local inexpensive model on the SME side. |
Domain Adversarial Fine-Tuning as an Effective Regularizer (2020.findings-emnlp)
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| Challenge: | Existing fine-tuning techniques can degrade general-domain representations . however, fine-timing can lead to catastrophic forgetting of knowledge . |
| Approach: | They propose a new regularization technique that complements the task-specific loss used during fine-tuning with an adversarial objective. |
| Outcome: | Empirical results show that AFTER improves performance on various natural language understanding tasks compared to standard fine-tuning. |
Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems (2024.findings-acl)
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| Challenge: | Creating effective task-oriented dialog systems is challenging due to the scarcity of training data. |
| Approach: | They empirically evaluate eight DA methods that have shown promising results in task-oriented dialog systems and other NLP systems. |
| Outcome: | The proposed methods have been successful in other NLP systems but not in the ToDSs. |
Still All Greeklish to Me: Greeklish to Greek Transliteration (2024.lrec-main)
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| Challenge: | Greeklish is a writing form that is used to avoid switching languages on multilingual keyboards . even native Greek speakers may struggle to understand Greeklished . |
| Approach: | They propose to use Greeklish to avoid switching languages on multilingual keyboards . they propose to train models on Greek datasets using the Greek alphabet . |
| Outcome: | The proposed model outperforms existing models on Greeklish data. |
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English (2022.acl-long)
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Ilias Chalkidis, Abhik Jana, Dirk Hartung, Michael Bommarito, Ion Androutsopoulos, Daniel Katz, Nikolaos Aletras
| Challenge: | Laws and their interpretations, legal arguments and agreements are typically expressed in writing. |
| Approach: | They propose a benchmark to evaluate model performance across legal NLU tasks . they also evaluate several generic and legal-oriented models . |
| Outcome: | The proposed model performs better across multiple tasks than previous models. |