Papers by Ruslan Mitkov
XAutoLM: Efficient Fine-Tuning of Language Models via Meta-Learning and AutoML (2025.emnlp-main)
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Ernesto Luis Estevanell Valladares, Suilan Estevez-Velarde, Yoan Gutierrez, Andrés Montoyo, Ruslan Mitkov
| Challenge: | XAutoLM is a meta-learning-augmented framework that can be used to optimize discriminative and generative LM fine-tuning pipelines. |
| Approach: | They propose a meta-learning-augmented AutoML framework that reuses past experiences to optimize discriminative and generative LM fine-tuning pipelines efficiently. |
| Outcome: | XAutoLM surpasses zero-shot optimizer’s peak F1 on five of six tasks, reduces mean evaluation time of pipelines by up to 4.5x, and uncovers 50% more pipelines above zero- shot Pareto front. |
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions (N19-1)
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| Challenge: | Existing approaches to identify discontinuous multiword expressions are limited in dealing with discontinuous occurrences. |
| Approach: | They propose a method to tag Multiword Expressions using a language-independent deep learning architecture to target discontinuity. |
| Outcome: | The proposed model outperforms baseline models on a multilingual dataset and scores higher than baseline models. |
Sinhala Encoder-only Language Models and Evaluation (2025.acl-long)
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Tharindu Ranasinghe, Hansi Hettiarachchi, Nadeesha Chathurangi Naradde Vidana Pathirana, Damith Premasiri, Lasitha Uyangodage, Isuri Nanomi Arachchige, Alistair Plum, Paul Rayson, Ruslan Mitkov
| Challenge: | Recent advances in language models (LMs) have produced excellent results in many NLP tasks, but their effectiveness is highly dependent on available pre-training resources. |
| Approach: | They propose to collect the largest monolingual corpus for Sinhala and compile a benchmark and evaluate LMs on it. |
| Outcome: | The proposed language models outperform the popular multilingual LMs in downstream NLP tasks. |
Authorship Attribution of Late 19th Century Novels using GAN-BERT (2023.acl-srw)
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| Challenge: | Conventional techniques and neural networks are the two main authorship attribution methods. |
| Approach: | They used a dataset of late 19th century novels in English to fine-tune a transformer-based authorship attribution model using transfer learning. |
| Outcome: | The proposed model outperforms the existing model with 0.88 accuracy and F1 scores. |
MUSTS: MUltilingual Semantic Textual Similarity Benchmark (2025.acl-short)
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| Challenge: | Existing benchmarks for semantic textual similarity (STS) are limited to high-resource languages and do not include datasets annotated focusing on relatedness instead of similarity. |
| Approach: | They propose to evaluate multilingual semantic textual similarity benchmarks which span 13 languages and annotated datasets to evaluate and compare them. |
| Outcome: | The proposed method is the most comprehensive benchmark of multilingual STS methods. |
An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers (2021.acl-short)
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| Challenge: | Existing word-level quality estimation models require labelled data for each language pair and expensive maintenance. |
| Approach: | They propose to use multilingual QE models to generalise across languages . they propose to train models on other language pairs to predict word-level quality . |
| Outcome: | The proposed models generalise well across languages, making them more useful in real-world scenarios. |
DORE: A Dataset for Portuguese Definition Generation (2024.lrec-main)
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| Challenge: | Definition modelling (DM) is the task of automatically generating a dictionary definition of a specific word. |
| Approach: | They propose to create a dataset for definition modelling for Portuguese with 100,000 definitions and evaluate several deep learning based DM models on the dataset. |
| Outcome: | The proposed dataset will facilitate research and study of Portuguese in wider contexts. |
TransQuest: Translation Quality Estimation with Cross-lingual Transformers (2020.coling-main)
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| Challenge: | Recent advances in the field of sentence-level quality estimation (QE) are based on neural-based architectures that require resourceintensive training. |
| Approach: | They propose a framework for sentence-level quality estimation based on cross-lingual transformers and use it to implement and evaluate two different neural architectures. |
| Outcome: | The proposed framework outperforms open-source QE frameworks when trained on WMT datasets and is very competitive in transfer learning settings. |
SmartMatch: Real-Time Semantic Retrieval for Translation Memory Systems (2026.eacl-demo)
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Ernesto L. Estevanell-Valladares, Salima Lamsiyah, Alicia Picazo-Izquierdo, Tharindu Ranasinghe, Ruslan Mitkov, Rafael Muñoz
| Challenge: | Translation Memory (TM) systems are core components of computer-aided translation tools . however, they fail to retrieve semantically relevant content when surface similarity is low. |
| Approach: | They propose an open-source demo and evaluation toolkit for TM retrieval that connects modern sentence encoders and strong lexical/fuzzy baselines with a vector database. |
| Outcome: | The proposed toolkit exposes the end-to-end retrieval pipeline through a web-based UI for qualitative inspection and preference logging. |
Classifying Referential and Non-referential It Using Gaze (D18-1)
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| Challenge: | a particular problem for anaphora resolution systems is the pronoun it, which can be used both referentially and non-referentially. |
| Approach: | They use eye-tracking data to learn how humans perform disambiguation and use it to improve automatic classification. |
| Outcome: | The proposed system outperforms a baseline and outperformed linguistic-based approaches. |