@inproceedings{bencke-etal-2024-inferbr,
    title = "{I}nfer{BR}: A Natural Language Inference Dataset in {P}ortuguese",
    author = "Bencke, Luciana  and
      Pereira, Francielle Vasconcellos  and
      Santos, Moniele Kunrath  and
      Moreira, Viviane",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.793",
    pages = "9050--9060",
    abstract = "Natural Language Inference semantic concepts are central to all aspects of natural language meaning. Portuguese has few NLI-annotated datasets created through automatic translation followed by manual checking. The manual creation of NLI datasets is complex and requires many efforts that are sometimes unavailable. Thus, investments to produce good quality synthetic instances that could be used to train machine learning models for NLI are welcome. This work produced InferBR, an NLI dataset for Portuguese. We relied on a semiautomatic process to generate premises and an automatic process to generate hypotheses. The dataset was manually revised, showing that 97.4{\%} of the sentence pairs had good quality, and nearly 100{\%} of the instances had the correct label assigned. The model trained with InferBR is better at recognizing entailment classes in the other Portuguese datasets than the reverse. Because of its diversity and many unique sentences, InferBR can potentially be further augmented. In addition to the dataset, a key contribution is our proposed generation processes for premises and hypotheses that can easily be adapted to other languages and tasks.",
}

Natural Language Inference semantic concepts are central to all aspects of natural language meaning. Portuguese has few NLI-annotated datasets created through automatic translation followed by manual checking. The manual creation of NLI datasets is complex and requires many efforts that are sometimes unavailable. Thus, investments to produce good quality synthetic instances that could be used to train machine learning models for NLI are welcome. This work produced InferBR, an NLI dataset for Portuguese. We relied on a semiautomatic process to generate premises and an automatic process to generate hypotheses. The dataset was manually revised, showing that 97.4{\%} of the sentence pairs had good quality, and nearly 100{\%} of the instances had the correct label assigned. The model trained with InferBR is better at recognizing entailment classes in the other Portuguese datasets than the reverse. Because of its diversity and many unique sentences, InferBR can potentially be further augmented. In addition to the dataset, a key contribution is our proposed generation processes for premises and hypotheses that can easily be adapted to other languages and tasks.
Bencke, Luciana Pereira, Francielle Vasconcellos Santos, Moniele Kunrath Moreira, Viviane
InferBR: A Natural Language Inference Dataset in Portuguese