Rossana Cunha


2021

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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.

2020

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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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Proceedings of the The Fourth Widening Natural Language Processing Workshop
Rossana Cunha | Samira Shaikh | Erika Varis | Ryan Georgi | Alicia Tsai | Antonios Anastasopoulos | Khyathi Raghavi Chandu
Proceedings of the The Fourth Widening Natural Language Processing Workshop

2019

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Proceedings of the 2019 Workshop on Widening NLP
Amittai Axelrod | Diyi Yang | Rossana Cunha | Samira Shaikh | Zeerak Waseem
Proceedings of the 2019 Workshop on Widening NLP