Marcio Lima Inácio

Also published as: Marcio Lima Inacio, Marcio Lima Inácio


2024

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Using Large Language Models for Identifying Satirical News in Brazilian Portuguese
Gabriela Wick-Pedro | Cássio Faria da Silva | Marcio Lima Inácio | Oto Araújo Vale | Helena de Medeiros Caseli
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1

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Exploring Multimodal Models for Humor Recognition in Portuguese
Marcio Lima Inácio | Hugo Gonçalo Oliveira
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1

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Investigating Paraphrase Generation as a Data Augmentation Strategy for Low-Resource AMR-to-Text Generation
Marco Antonio Sobrevilla Cabezudo | Marcio Lima Inacio | Thiago Alexandre Salgueiro Pardo
Proceedings of the 17th International Natural Language Generation Conference

Abstract Meaning Representation (AMR) is a meaning representation (MR) designed to abstract away from syntax, allowing syntactically different sentences to share the same AMR graph. Unlike other MRs, existing AMR corpora typically link one AMR graph to a single reference. This paper investigates the value of paraphrase generation in low-resource AMR-to-Text generation by testing various paraphrase generation strategies and evaluating their impact. The findings show that paraphrase generation significantly outperforms the baseline and traditional data augmentation methods, even with fewer training instances. Human evaluations indicate that this strategy often produces syntactic-based paraphrases and can exceed the performance of previous approaches. Additionally, the paper releases a paraphrase-extended version of the AMR corpus.

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Puntuguese: A Corpus of Puns in Portuguese with Micro-edits
Marcio Lima Inacio | Gabriela Wick-Pedro | Renata Ramisch | Luís Espírito Santo | Xiomara S. Q. Chacon | Roney Santos | Rogério Sousa | Rafael Anchiêta | Hugo Goncalo Oliveira
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Humor is an intricate part of verbal communication and dealing with this kind of phenomenon is essential to building systems that can process language at large with all of its complexities. In this paper, we introduce Puntuguese, a new corpus of punning humor in Portuguese, motivated by previous works showing that currently available corpora for this language are still unfit for Machine Learning due to data leakage. Puntuguese comprises 4,903 manually-gathered punning one-liners in Brazilian and European Portuguese. To create negative examples that differ exclusively in terms of funniness, we carried out a micro-editing process, in which all jokes were edited by fluent Portuguese speakers to make the texts unfunny. Finally, we did some experiments on Humor Recognition, showing that Puntuguese is considerably more difficult than the previous corpus, achieving an F1-Score of 68.9%. With this new dataset, we hope to enable research not only in NLP but also in other fields that are interested in studying humor; thus, the data is publicly available.

2023

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Towards Generation and Recognition of Humorous Texts in Portuguese
Marcio Lima Inácio | Hugo Gonçalo Oliveira
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop

Dealing with humor is an important step to develop Natural Language Processing tools capable of handling sophisticated semantic and pragmatic knowledge. In this context, this PhD thesis focuses on the automatic generation and recognition of verbal punning humor in Portuguese, which is still an underdeveloped language when compared to English. One of the main goals of this research is to conciliate Natural Language Generation computational models with existing theories of humor from the Humanities while avoiding mere generation by including contextual information into the generation process. Another point that is of utmost importance is the inclusion of the listener as an active part in the process of understanding and creating humor; we hope to achieve this by using concepts from Recommender Systems in our methods. Ultimately, we want to not only advance the current state-of-the-art in humor generation and recognition, but also to help the general Portuguese-speaking research community with methods, tools and resources that may aid in the development of further techniques for this language. We also expect our systems to provide insightful ideas about how humor is created and perceived by both humans and machines.

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What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models
Marcio Lima Inácio | Gabriela Wick-pedro | Hugo Goncalo Oliveira
Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

Towards computational systems capable of dealing with complex and general linguistic phenomena, it is essential to understand figurative language, which verbal humor is an instance of. This paper reports state-of-the-art results for Humor Recognition in Portuguese, specifically, an F1-score of 99.64% with a BERT-based classifier. However, following the surprising high performance in such a challenging task, we further analyzed what was actually learned by the classifiers. Our main conclusions were that classifiers based on content-features achieve the best performance, but rely mostly on stylistic aspects of the text, not necessarily related to humor, such as punctuation and question words. On the other hand, for humor-related features, we identified some important aspects, such as the presence of named entities, ambiguity and incongruity.

2021

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Semantic-Based Opinion Summarization
Marcio Lima Inácio | Thiago Pardo
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

The amount of information available online can be overwhelming for users to digest, specially when dealing with other users’ comments when making a decision about buying a product or service. In this context, opinion summarization systems are of great value, extracting important information from the texts and presenting them to the user in a more understandable manner. It is also known that the usage of semantic representations can benefit the quality of the generated summaries. This paper aims at developing opinion summarization methods based on Abstract Meaning Representation of texts in the Brazilian Portuguese language. Four different methods have been investigated, alongside some literature approaches. The results show that a Machine Learning-based method produced summaries of higher quality, outperforming other literature techniques on manually constructed semantic graphs. We also show that using parsed graphs over manually annotated ones harmed the output. Finally, an analysis of how important different types of information are for the summarization process suggests that using Sentiment Analysis features did not improve summary quality.

2020

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NMT and PBSMT Error Analyses in English to Brazilian Portuguese Automatic Translations
Helena Caseli | Marcio Lima Inácio
Proceedings of the Twelfth Language Resources and Evaluation Conference

Machine Translation (MT) is one of the most important natural language processing applications. Independently of the applied MT approach, a MT system automatically generates an equivalent version (in some target language) of an input sentence (in some source language). Recently, a new MT approach has been proposed: neural machine translation (NMT). NMT systems have already outperformed traditional phrase-based statistical machine translation (PBSMT) systems for some pairs of languages. However, any MT approach outputs errors. In this work we present a comparative study of MT errors generated by a NMT system and a PBSMT system trained on the same English – Brazilian Portuguese parallel corpus. This is the first study of this kind involving NMT for Brazilian Portuguese. Furthermore, the analyses and conclusions presented here point out the specific problems of NMT outputs in relation to PBSMT ones and also give lots of insights into how to implement automatic post-editing for a NMT system. Finally, the corpora annotated with MT errors generated by both PBSMT and NMT systems are also available.