Papers by Fernando Alva-Manchego
Strong Baselines for Complex Word Identification across Multiple Languages (N19-1)
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Pierre Finnimore, Elisabeth Fritzsch, Daniel King, Alison Sneyd, Aneeq Ur Rehman, Fernando Alva-Manchego, Andreas Vlachos
| Challenge: | Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a specific type of reader. |
| Approach: | They propose to use monolingual and cross-lingual CWI models to make predictions for languages not seen during training. |
| Outcome: | The proposed models perform as well as (or better than) most models submitted to the latest CWI Shared Task. |
Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing (2022.emnlp-main)
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| Challenge: | Existing methods to represent study programs using bag-of-words and clustering algorithms are prone to biases due to personal beliefs and perspectives. |
| Approach: | They propose to use pre-trained word embeddings to fine-tune a study program classification task to obtain more accurate curriculum representations than strong baselines. |
| Outcome: | The proposed method is compared to existing methods on a study program classification task and on comparing computing curricula from USA and Latin America. |
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations (2020.acl-main)
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| Challenge: | Existing models for sentence simplification are focused on a single transformation, such as lexical paraphrasing or splitting. |
| Approach: | They propose a dataset for assessing sentence simplification in English using a crowdsourced multi-reference corpus. |
| Outcome: | The proposed dataset shows that it captures characteristics of simplicity better than other datasets. |
Adapting Sentence-level Automatic Metrics for Document-level Simplification Evaluation (2025.naacl-long)
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| Challenge: | Existing studies on text simplification have focused on sentence simplification, but these metrics often underperform on longer texts. |
| Approach: | They propose to adapt existing sentence-level metrics for paragraph- or document-level simplification by incorporating a new approach to the evaluation of text simplification metrics. |
| Outcome: | The proposed approach outperforms existing sentence-level metrics in terms of correlation with human judgment and the sensitivity and robustness of various metrics to different types of errors produced by existing systems. |
An Empirical Comparison of LM-based Question and Answer Generation Methods (2023.findings-acl)
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| Challenge: | Question and answer generation (QAG) is a task of generating question-answer pairs given a context. |
| Approach: | They propose to leverage sequence-to-sequence language model fine-tuning to generate question-answer pairs given a context. |
| Outcome: | The proposed model outperforms other more convoluted approaches in the end-to-end model and is computationally light at both training and inference times. |
A Practical Toolkit for Multilingual Question and Answer Generation (2023.acl-demo)
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| Challenge: | Generating questions and answers from text is a challenging task due to the expected structured output. |
| Approach: | They propose an online service for multilingual QAG along with a python package for model fine-tuning, generation, and evaluation. |
| Outcome: | The proposed model is available in eight languages and can be used online or locally via lmqg. |
Knowledge Distillation for Quality Estimation (2021.findings-acl)
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Amit Gajbhiye, Marina Fomicheva, Fernando Alva-Manchego, Frédéric Blain, Abiola Obamuyide, Nikolaos Aletras, Lucia Specia
| Challenge: | Recent success in Quality Estimation stems from the use of multilingual pre-trained models, where large models lead to impressive results. |
| Approach: | They propose to transfer knowledge from a strong QE teacher model to a much smaller model with a different, shallower architecture. |
| Outcome: | The proposed model performs better than distilled models with 8x fewer parameters. |
Neural Readability Pairwise Ranking for Sentences in Italian Administrative Language (2022.aacl-main)
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| Challenge: | Automatic Readability Assessment (ARA) aims at assigning a complexity level to a text, which could help improve the accessibility to information in specific domains, such as the administrative one. |
| Approach: | They propose to use a Neural Pairwise Ranking Model (NPRM) to assess Italian administrative texts by rewriting sentences using three different rewrite strategies. |
| Outcome: | The proposed model is effective in zero-shot scenarios (0.78) and the best results are obtained by adding in-domain data (achieving perfect performance for such sentence pairs). |
BLESS: Benchmarking Large Language Models on Sentence Simplification (2023.emnlp-main)
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Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez, Sweta Agrawal, Dennis Aumiller, Fernando Alva-Manchego, Matthew Shardlow
| Challenge: | BLESS is a performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS). |
| Approach: | They present a performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS). |
| Outcome: | The proposed benchmarks show that the most recent state-of-the-art LLMs perform better on the task of text simplification (TS). |
Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts (2022.lrec-1)
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| Challenge: | Specialist high-quality information is typically first available in English, and it is written in a language that may be difficult to understand by most readers. |
| Approach: | They propose to use a new language resource to simplify COVID-19 texts . they propose to employ four annotators who simplified over 6,000 sentences . |
| Outcome: | The proposed dataset improves readability from the original texts to their simplified versions. |
deepQuest-py: Large and Distilled Models for Quality Estimation (2021.emnlp-demo)
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Fernando Alva-Manchego, Abiola Obamuyide, Amit Gajbhiye, Frédéric Blain, Marina Fomicheva, Lucia Specia
| Challenge: | Quality Estimation (QE) is a tool for machine translation that predicts how good translations are without comparing them to gold-standard references. |
| Approach: | They introduce a framework for training and evaluation of large and light-weight models for Quality Estimation (QE) they use pre-trained Transformers to train large and efficient QE models. |
| Outcome: | The framework provides access to state-of-the-art models based on pre-trained Transformers for sentence-level and word-level QE and a web interface for testing and visualising their predictions. |
Generative Language Models for Paragraph-Level Question Generation (2022.emnlp-main)
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| Challenge: | Powerful generative models have led to recent progress in question generation. |
| Approach: | They propose a multilingual and multidomain benchmark for question generation that unifies existing datasets by converting them to a standard QG setting. |
| Outcome: | The proposed benchmark unifies existing question answering datasets to a QG setting. |
EASSE: Easier Automatic Sentence Simplification Evaluation (D19-3)
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| Challenge: | EASSE provides access to a broad range of evaluation resources including standard automatic metrics, word-level accuracy scores and reference-independent quality estimation features. |
| Approach: | They propose to provide a Python package that provides access to automatic evaluation and comparison of Sentence Simplification (SS) systems. |
| Outcome: | The proposed tool allows comparison and understanding of the performance of Sentence Simplification (SS) systems. |
Controllable Text Simplification with Explicit Paraphrasing (2021.naacl-main)
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| Challenge: | Existing text simplification systems rely on deletion and do not paraphrase well. |
| Approach: | They propose a hybrid approach that leverages linguistically-motivated rules for splitting and deletion and couples them with a neural paraphrasing model to produce varied rewriting styles. |
| Outcome: | The proposed model improves paraphrasing capability and paraphrases more often than existing models. |
UniversalCEFR: Enabling Open Multilingual Research on Language Proficiency Assessment (2025.emnlp-main)
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Joseph Marvin Imperial, Abdullah Barayan, Regina Stodden, Rodrigo Wilkens, Ricardo Muñoz Sánchez, Lingyun Gao, Melissa Torgbi, Dawn Knight, Gail Forey, Reka R. Jablonkai, Ekaterina Kochmar, Robert Joshua Reynolds, Eugénio Ribeiro, Horacio Saggion, Elena Volodina, Sowmya Vajjala, Thomas François, Fernando Alva-Manchego, Harish Tayyar Madabushi
| Challenge: | Language proficiency research plays a central role in education and often intersects with advances in linguistics and AI. |
| Approach: | They propose a multilingual multidimensional dataset of texts annotated according to the CEFR scale in 13 languages. |
| Outcome: | The proposed dataset supports linguistic features and pretrained models in multilingual CEFR level assessment. |
Analysing Zero-Shot Readability-Controlled Sentence Simplification (2025.coling-main)
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| Challenge: | Text simplification (RCTS) models often depend on parallel corpora with readability annotations on both source and target sides. |
| Approach: | They propose to use instruction-tuned large language models for zero-shot RCTS to reduce reliance on parallel corpora with readability annotations on both source and target sides. |
| Outcome: | The proposed model can generate sentences with the desired readability, but the model's limitations and characteristics of the source sentences impede it. |