Papers with Brazilian Portuguese
Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet (2022.lrec-1)
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| Challenge: | Neural models have been advancing in a myriad of tasks, but there is a lack of large training data. |
| Approach: | They propose a method for domain adaptation of Neural Machine Translation systems using a multilingual FrameNet enriched with qualia relations as an external knowledge base. |
| Outcome: | The proposed system outperforms the state-of-the-art commercial system in an experiment . the proposed system substitutes domain-specific terms in the source language by their adequate translation in the target language. |
Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language (P19-2)
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| Challenge: | Natural Language Generation (NLG) is a promising area in Natural Language Processing (NLP) . |
| Approach: | They present a review of the literature on Natural Language Generation in Brazilian Portuguese. |
| Outcome: | The proposed approaches are based on the Abstract Meaning Representation formalism and have potential future directions. |
Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations (D19-63)
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| Challenge: | Abstract Meaning Representation and Brazilian Portuguese (BP) are selected as semantic representation and language, respectively. |
| Approach: | They propose to use Brazilian Portuguese and Abstract Meaning Representation as semantic representations for NLG. |
| Outcome: | The proposed methods were evaluated on two datasets (one automatically generated and another human-generated) to compare the performance in a real context. |
Towards Personalised and Document-level Machine Translation of Dialogue (2021.eacl-srw)
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| Challenge: | State-of-the-art (SOTA) neural machine translation systems translate texts at sentence level, ignoring context. |
| Approach: | They propose to integrate extra-textual information into the translation process for the domain of dialogue extracted from TV subtitles in five languages: English, Brazilian Portuguese, German, French and Polish. |
| Outcome: | The proposed systems translate texts at sentence level, ignoring context . there are no readily available robust evaluation metrics for them . |
Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis (2020.aacl-main)
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| Challenge: | Hate speech and toxic comments are a common concern of social media platform users . identifying toxic comments is important for studying and preventing the proliferation of toxicity in social media. |
| Approach: | They propose to use Brazilian Portuguese to analyze toxic or non-toxic tweets . they propose to analyze tweets as toxic or in different types of toxicity . |
| Outcome: | The proposed model achieves 76% macro-F1 score using monolingual data in the binary case. |
BlogSet-BR: A Brazilian Portuguese Blog Corpus (L18-1)
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| Challenge: | Several efforts have been made to build a corpus based on user-generated content . however, there is still a lack of a large semi-structured corpus that also contains author profiles in Brazilian Portuguese. |
| Approach: | They propose to build a Brazilian Portuguese corpus with 2.1 billion words extracted from 7.4 million posts over 808 thousand different Brazilian blogs. |
| Outcome: | The proposed corpus contains 2.1 billion words extracted from 7.4 million posts over 808 thousand different Brazilian blogs. |
Towards AMR-BR: A SemBank for Brazilian Portuguese Language (L18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a recent and prominent meaning representation with good acceptance and several applications in the Natural Language Processing area. |
| Approach: | They propose to build an AMR annotated corpus for Brazilian Portuguese using an alignment-based approach. |
| Outcome: | The proposed corpus is based on the Little Prince book, which went into the public domain and explored some language-specific annotation issues. |
Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer (2021.naacl-main)
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| Challenge: | XFORMAL benchmarks formal reformulations of informal text in Brazilian Portuguese, French, and Italian . most work on style transfer within English, while covering different languages has received disproportional interest. |
| Approach: | They create a benchmark of multiple formal reformulations of informal text in Brazil, Brazil, and Italy. |
| Outcome: | XFORMAL benchmarks formal reformulations of informal text in Brazilian Portuguese, French, and Italian . results show that state-of-the-art approaches perform close to simple baselines . |
Computing with Subjectivity Lexicons (2020.lrec-1)
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Caio L. M. Jeronimo, Claudio E. C. Campelo, Leandro Balby Marinho, Allan Sales, Adriano Veloso, Roberta Viola
| Challenge: | a new set of lexicons for expressing subjectivity in text documents is presented . lexiconics are useful resources for identifying semantics relevant to sentiment, emotion, personality, language bias, mood, and attitude. |
| Approach: | They propose a set of lexicons for expressing subjectivity in Brazilian Portuguese text documents . they use word embedding techniques to capture semantically related words to the ones in the lexicos . |
| Outcome: | The proposed lexicons represent different subjectivity dimensions and are more compact in number of terms. |
MuPe Life Stories Dataset: Spontaneous Speech in Brazilian Portuguese with a Case Study Evaluation on ASR Bias against Speakers Groups and Topic Modeling (2025.coling-main)
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Sidney Evaldo Leal, Arnaldo Candido Junior, Ricardo Marcacini, Edresson Casanova, Odilon Gonçalves, Anderson Silva Soares, Rodrigo Freitas Lima, Lucas Rafael Stefanel Gris, Sandra Aluísio
| Challenge: | Recent datasets for automatic speech recognition in Brazilian Portuguese lack diversity in terms of age groups, regional accents, and education levels. |
| Approach: | They propose to use a dataset to analyze the impact of ASR in Brazilian Portuguese (BP) they demonstrate that current models are biased regarding age, education, and regional accents. |
| Outcome: | The proposed dataset helps mitigate biases in current ASR models regarding education levels and age groups. |
NMT and PBSMT Error Analyses in English to Brazilian Portuguese Automatic Translations (2020.lrec-1)
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| Challenge: | Recent work proposes neural machine translation (NMT) for Brazilian Portuguese. |
| Approach: | They propose a neural machine translation approach that generates equivalent sentences in target language and source language. |
| Outcome: | The proposed approach outperforms phrase-based statistical machine translation systems for some pairs of languages. |
RDF2PT: Generating Brazilian Portuguese Texts from RDF Data (L18-1)
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Diego Moussallem, Thiago Ferreira, Marcos Zampieri, Maria Claudia Cavalcanti, Geraldo Xexéo, Mariana Neves, Axel-Cyrille Ngonga Ngomo
| Challenge: | Existing approaches to generate natural language from RDF data have been proposed to generate texts in Brazilian Portuguese. |
| Approach: | They propose a rule-based approach to verbalize RDF data to Brazilian Portuguese language. |
| Outcome: | The proposed approach generates text similar to that generated by humans and can hence be easily understood. |
Using Eye-tracking Data to Predict the Readability of Brazilian Portuguese Sentences in Single-task, Multi-task and Sequential Transfer Learning Approaches (2020.coling-main)
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Sidney Evaldo Leal, João Marcos Munguba Vieira, Erica dos Santos Rodrigues, Elisângela Nogueira Teixeira, Sandra Aluísio
| Challenge: | Sentence complexity assessment is a relatively new task in Natural Language Processing. |
| Approach: | They propose to use Brazilian Portuguese to evaluate sentences with linguistic features to improve readability. |
| Outcome: | The proposed model reaches the state-of-the-art for Brazilian Portuguese with 97.8% accuracy with linguistic features. |
ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification (2022.coling-1)
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| Challenge: | Lexical simplification (LS) is the task of replacing complex words with simpler alternatives to make texts more accessible to various target populations. |
| Approach: | They propose to use a Brazilian Portuguese multi-candidate dataset to test LS systems. |
| Outcome: | The proposed model outperforms existing models on Brazilian Portuguese and Brazilian newspaper articles. |
Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing (2020.coling-main)
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| Challenge: | Existing corpus for automatic post-editing of English and Brazilian Portuguese is limited. |
| Approach: | They introduce a corpus for Automatic Post-Editing of English and Brazilian Portuguese. |
| Outcome: | The proposed corpus improves on the English and Brazilian Portuguese languages. |
Framed Multi30K: A Frame-Based Multimodal-Multilingual Dataset (2024.lrec-main)
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Marcelo Viridiano, Arthur Lorenzi, Tiago Timponi Torrent, Ely E. Matos, Adriana S. Pagano, Natália Sathler Sigiliano, Maucha Gamonal, Helen de Andrade Abreu, Lívia Vicente Dutra, Mairon Samagaio, Mariane Carvalho, Franciany Campos, Gabrielly Azalim, Bruna Mazzei, Mateus Fonseca de Oliveira, Ana Carolina Luz, Livia Padua Ruiz, Júlia Bellei, Amanda Pestana, Josiane Costa, Iasmin Rabelo, Anna Beatriz Silva, Raquel Roza, Mariana Souza Mota, Igor Oliveira, Márcio Henrique Pelegrino de Freitas
| Challenge: | Recent advances in image-captioning datasets combine image and language to solve a diverse range of tasks. |
| Approach: | They propose a Brazilian Portuguese multimodal-multilingual dataset that extends the Multi30K dataset with 158,915 original Brazilian Portuguese descriptions and 30,104 Brazilian Portuguese translations. |
| Outcome: | The proposed dataset adds 2,677,613 frame evocation labels to the 158,915 English descriptions and to the ones created for Brazilian Portuguese. |
Building a Sentiment Corpus of Tweets in Brazilian Portuguese (L18-1)
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| Challenge: | Sentiment analysis is a popular area of Natural Language Processing due to its subjective and semantic characteristics. |
| Approach: | They propose to annotate Brazilian Portuguese sentences manually using a sentiment corpus . they run experiments on polarity classification using six machine learning classifiers . |
| Outcome: | The proposed method is based on a Brazilian Portuguese sentiment corpus and achieved 80.38% on F-Measure and 64.87% when including the neutral class. |
The brWaC Corpus: A New Open Resource for Brazilian Portuguese (L18-1)
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| Challenge: | a large corpus for Brazilian Portuguese is needed for NLP applications . the corpus is 2.7 billion tokens, and domain diversity is maximized . |
| Approach: | They propose to build a large Web corpus for Brazilian Portuguese with 2.7 billion tokens . they also propose an updated sentence-level approach for the strict removal of duplicated content . |
| Outcome: | The proposed corpus is based on a pipeline methodology and is available for querying and downloading. |
Language Variety Identification with True Labels (2024.lrec-main)
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Marcos Zampieri, Kai North, Tommi Jauhiainen, Mariano Felice, Neha Kumari, Nishant Nair, Yash Mahesh Bangera
| Challenge: | Language identification datasets are compiled with the assumption that the gold label of each instance is determined by where texts are retrieved from. |
| Approach: | They present a human-annotated multilingual dataset for language variety identification . they use a model to train multiple models to discriminate between different languages . |
| Outcome: | The proposed dataset provides a reliable benchmark toward robust and fairer language variety identification systems. |
Multilingual Generation and Answering of Questions from Texts and Knowledge Graphs (2023.findings-emnlp)
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| Challenge: | Existing methods for QG-QA are limited to English, but can be used in other languages. |
| Approach: | They propose to bring multilinguality to multimodal QG-QA by using Brazilian Portuguese and Russian data. |
| Outcome: | The proposed approach outperforms a baseline on English and can handle both languages. |
The Harmonic Structure of Information Contours (2025.acl-long)
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Eleftheria Tsipidi, Samuel Kiegeland, Franz Nowak, Tianyang Xu, Ethan Wilcox, Alex Warstadt, Ryan Cotterell, Mario Giulianelli
| Challenge: | Language typically does not maintain a uniform information rate, but it fluctuates around a global average . a new study suggests periodicity may be a factor in information rate oscillations . |
| Approach: | They propose a hypothesis that language does not maintain a uniform information rate . they apply harmonic regression and introduce a new extension to detect periodicity . |
| Outcome: | The proposed method reveals that language oscillates at periodic intervals across frequencies . it also offers a framework for uncovering structural pressures at various levels of linguistic granularity. |
LangMark: A Multilingual Dataset for Automatic Post-Editing (2025.acl-long)
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Diego Velazquez, Mikaela Grace, Konstantinos Karageorgos, Lawrence Carin, Aaron Schliem, Dimitrios Zaikis, Roger Wechsler
| Challenge: | Automated post-editing (APE) aims to correct errors in machine-translated text . lack of large-scale multilingual datasets specifically tailored to NMT outputs hinders APE development . |
| Approach: | They propose to use a human-annotated multilingual APE dataset for English translation to seven languages to address this gap. |
| Outcome: | The proposed dataset offers both linguistic diversity and scale. |
Self-Explaining Hate Speech Detection with Moral Rationales (2026.findings-acl)
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Francielle Vargas, Jackson Trager, Diego Alves, Matteo Guida, Surendrabikram Thapa, Berk Atıl, Daryna Dementieva, Andrew J Smart, Ameeta Agrawal
| Challenge: | Existing models for hate speech detection are opaque and rely on surface-level cues. Existing approaches often encode biases originating from training data and annotation processes. |
| Approach: | They propose a framework that integrates moral rationale supervision into training . they propose SMRA for self-explaining hate speech detection . |
| Outcome: | The proposed framework improves performance across binary hate speech detection and multi-label moral sentiment classification. |
Multi-LMentry: Can Multilingual LLMs Solve Elementary Tasks Across Languages? (2025.emnlp-main)
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Luca Moroni, Javier Aula-Blasco, Simone Conia, Irene Baucells, Naiara Perez, Silvia Paniagua Suárez, Anna Sallés, Malte Ostendorff, Júlia Falcão, Guijin Son, Aitor Gonzalez-Agirre, Roberto Navigli, Marta Villegas
| Challenge: | a recent study focused on complex, high-level tasks, but LMentry is limited to English . a multilingual evaluation of large language models is needed to address this gap, authors say . |
| Approach: | They propose a compact benchmark that enables systematic evaluation of large language models . they propose to use tasks that are trivial for humans but remain surprisingly difficult for LLMs . |
| Outcome: | The proposed benchmark is limited to English, leaving its insights linguistically narrow. |