Marcio Lima Inacio, Gabriela Wick-Pedro, Renata Ramisch, Luís Espírito Santo, Xiomara S. Q. Chacon, Roney Santos, Rogério Sousa, Rafael Anchiêta, Hugo Goncalo Oliveira
| Challenge: | Existing corpus of punning humor in Portuguese is unfit for machine learning due to data leakage. |
| Approach: | They propose to use Puntuguese to create a corpus of punning humor in Portuguese that is significantly more difficult to recognize than the previous corpus. |
| Outcome: | The proposed corpus achieves an F1-Score of 68.9% and is significantly more difficult than the previous corpus. |
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Towards Generation and Recognition of Humorous Texts in Portuguese (2023.eacl-srw)
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| Challenge: | This PhD thesis focuses on the automatic generation and recognition of verbal punning humor in Portuguese. |
| Approach: | They propose to combine natural language generation and cognitive processing to generate and recognize verbal humor in Portuguese. |
| Outcome: | The proposed methods aim to generate and recognize humor in Portuguese, an underdeveloped language compared to English. |
Corpora and Baselines for Humour Recognition in Portuguese (2020.lrec-1)
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| Challenge: | Existing work on the recognition of verbal humour in Portuguese has not been done . humor recognition is a sign of fluency in a language, and is not yet widely used in other languages. |
| Approach: | They propose to create three corpora covering two styles of humour and four sources of non-humorous text that are used for testing computational models. |
| Outcome: | The proposed models can be used to train and test models in Portuguese, and may be used as baselines for future projects. |
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. |
Large Dataset and Language Model Fun-Tuning for Humor Recognition (P19-1)
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| Challenge: | Humor recognition datasets contain only English texts and focus on puns. |
| Approach: | They collected a dataset of jokes and funny dialogues in Russian and complemented them carefully with unfunny texts with similar lexical properties. |
| Outcome: | The proposed method is based on the universal language model finetuning and has an F1 score of 0.91 on a test set. |
HAHA 2019 Dataset: A Corpus for Humor Analysis in Spanish (2020.lrec-1)
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| Challenge: | 30,000 Spanish tweets were crowd-annotated with humor value and funniness score . the corpus contains approximately 38.6% of humorous tweets with an average score of 2.04 in a scale from 1 to 5 for the humorous tweet. |
| Approach: | They develop a corpus of 30,000 Spanish tweets crowd-annotated with humor value and funniness score. |
| Outcome: | The results obtained from the 30,000 tweets in the Spanish language are encouraging. |
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. |
AIA-BDE: A Corpus of FAQs in Portuguese and their Variations (2020.lrec-1)
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Hugo Gonçalo Oliveira, João Ferreira, José Santos, Pedro Fialho, Ricardo Rodrigues, Luisa Coheur, Ana Alves
| Challenge: | a corpus of 380 domain-oriented FAQs in Portuguese is presented . paraphrases or entailed questions are created manually, by humans, or automatically, with Google Translate. |
| Approach: | They present a corpus of 380 domain-oriented FAQs in Portuguese and their variations, i.e., paraphrases or entailed questions, created manually, by humans, or automatically, with Google Translate. |
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A Survey of Pun Generation: Datasets, Evaluations and Methodologies (2025.findings-emnlp)
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| Challenge: | Pun generation aims to modify linguistic elements in text to produce humour or evoke double meanings. |
| Approach: | They propose to review pun generation datasets and methods across different stages . pun generation aims to produce humour or evoke double meanings . |
| Outcome: | This paper summarises both automated and human evaluation metrics used to assess the quality of pun generation. |
Development and Validation of a Corpus for Machine Humor Comprehension (2020.lrec-1)
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| Challenge: | a Chinese humor corpus was labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Approach: | They develop a Chinese humor corpus with 3,365 jokes labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Outcome: | The proposed corpus contains 3,365 jokes from over 40 sources. |
PPORTAL_ner: An Annotated Corpus of Portuguese Literary Entities (2024.lrec-main)
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| Challenge: | Annotated corpus of 25 literary texts provides a rich set of annotations for Named Entity Recognition models. |
| Approach: | They propose an annotation dataset that simplifies the development of Named Entity Recognition models for Portuguese literary texts. |
| Outcome: | The proposed dataset simplifies the development of Named Entity Recognition models for Portuguese literary works. |