Adriana Pagano


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A Persona-Based Corpus in the Diabetes Self-Care Domain - Applying a Human-Centered Approach to a Low-Resource Context
Rossana Cunha | Thiago Castro Ferreira | Adriana Pagano | Fabio Alves
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

While Natural Language Processing (NLP) models have gained substantial attention, only in recent years has research opened new paths for tackling Human-Computer Design (HCD) from the perspective of natural language. We focus on developing a human-centered corpus, more specifically, a persona-based corpus in a particular healthcare domain (diabetes mellitus self-care). In order to follow an HCD approach, we created personas to model interpersonal interaction (expert and non-expert users) in that specific domain. We show that an HCD approach benefits language generation from different perspectives, from machines to humans - contributing with new directions for low-resource contexts (languages other than English and sensitive domains) where the need to promote effective communication is essential.

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Authorship attribution in translated texts: a stylometric approach to translator style
Ana Pagano | Carlos Perini | Evandro Cunha | Adriana Pagano
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 2


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Enriching the E2E dataset
Thiago Castro Ferreira | Helena Vaz | Brian Davis | Adriana Pagano
Proceedings of the 14th International Conference on Natural Language Generation

This study introduces an enriched version of the E2E dataset, one of the most popular language resources for data-to-text NLG. We extract intermediate representations for popular pipeline tasks such as discourse ordering, text structuring, lexicalization and referring expression generation, enabling researchers to rapidly develop and evaluate their data-to-text pipeline systems. The intermediate representations are extracted by aligning non-linguistic and text representations through a process called delexicalization, which consists in replacing input referring expressions to entities/attributes with placeholders. The enriched dataset is publicly available.

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Evaluating Recognizing Question Entailment Methods for a Portuguese Community Question-Answering System about Diabetes Mellitus
Thiago Castro Ferreira | João Victor de Pinho Costa | Isabela Rigotto | Vitoria Portella | Gabriel Frota | Ana Luisa A. R. Guimarães | Adalberto Penna | Isabela Lee | Tayane A. Soares | Sophia Rolim | Rossana Cunha | Celso França | Ariel Santos | Rivaney F. Oliveira | Abisague Langbehn | Daniel Hasan Dalip | Marcos André Gonçalves | Rodrigo Bastos Fóscolo | Adriana Pagano
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

This study describes the development of a Portuguese Community-Question Answering benchmark in the domain of Diabetes Mellitus using a Recognizing Question Entailment (RQE) approach. Given a premise question, RQE aims to retrieve semantically similar, already answered, archived questions. We build a new Portuguese benchmark corpus with 785 pairs between premise questions and archived answered questions marked with relevance judgments by medical experts. Based on the benchmark corpus, we leveraged and evaluated several RQE approaches ranging from traditional information retrieval methods to novel large pre-trained language models and ensemble techniques using learn-to-rank approaches. Our experimental results show that a supervised transformer-based method trained with multiple languages and for multiple tasks (MUSE) outperforms the alternatives. Our results also show that ensembles of methods (stacking) as well as a traditional (light) information retrieval method (BM25) can produce competitive results. Finally, among the tested strategies, those that exploit only the question (not the answer), provide the best effectiveness-efficiency trade-off. Code is publicly available.

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On auxiliary verb in Universal Dependencies: untangling the issue and proposing a systematized annotation strategy
Magali Duran | Adriana Pagano | Amanda Rassi | Thiago Pardo
Proceedings of the Sixth International Conference on Dependency Linguistics (Depling, SyntaxFest 2021)


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DaMata: A Robot-Journalist Covering the Brazilian Amazon Deforestation
André Luiz Rosa Teixeira | João Campos | Rossana Cunha | Thiago Castro Ferreira | Adriana Pagano | Fabio Cozman
Proceedings of the 13th International Conference on Natural Language Generation

This demo paper introduces DaMata, a robot-journalist covering deforestation in the Brazilian Amazon. The robot-journalist is based on a pipeline architecture of Natural Language Generation, which yields multilingual daily and monthly reports based on the public data provided by DETER, a real-time deforestation satellite monitor developed and maintained by the Brazilian National Institute for Space Research (INPE). DaMata automatically generates reports in Brazilian Portuguese and English and publishes them on the Twitter platform. Corpus and code are publicly available.

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Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities
Rossana Cunha | Thiago Castro Ferreira | Adriana Pagano | Fabio Alves
Proceedings of the 28th International Conference on Computational Linguistics

Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data. A core micro-planning task within NLG is referring expression generation (REG), which aims to automatically generate noun phrases to refer to entities mentioned as discourse unfolds. A limitation of novel REG models is not being able to generate referring expressions to entities not encountered during the training process. To solve this problem, we propose two extensions to NeuralREG, a state-of-the-art encoder-decoder REG model. The first is a copy mechanism, whereas the second consists of representing the gender and type of the referent as inputs to the model. Drawing on the results of automatic and human evaluation as well as an ablation study using the WebNLG corpus, we contend that our proposal contributes to the generation of more meaningful referring expressions to unseen entities than the original system and related work. Code and all produced data are publicly available.

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Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing
Felipe Almeida Costa | Thiago Castro Ferreira | Adriana Pagano | Wagner Meira
Proceedings of the 28th International Conference on Computational Linguistics

This paper introduces the first corpus for Automatic Post-Editing of English and a low-resource language, Brazilian Portuguese. The source English texts were extracted from the WebNLG corpus and automatically translated into Portuguese using a state-of-the-art industrial neural machine translator. Post-edits were then obtained in an experiment with native speakers of Brazilian Portuguese. To assess the quality of the corpus, we performed error analysis and computed complexity indicators measuring how difficult the APE task would be. We report preliminary results of Phrase-Based and Neural Machine Translation Models on this new corpus. Data and code publicly available in our repository.


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Estudo exploratório de categorias gramaticais com potencial de indicadores para a Análise de Sentimentos (An Exploratory study of grammatical categories as potential indicators for Sentiment Analysis)[In Portuguese]
Júlia Rodrigues | Adriana Pagano | Emerson Paraiso
Proceedings of the 11th Brazilian Symposium in Information and Human Language Technology