Sepúlveda-Torres, Robiert, Bonet-Jover, Alba, Saquete Boró, Estela
“Here Are the Rules: Ignore All Rules”: Automatic Contradiction Detection in Spanish
Sepúlveda-Torres R, Bonet-Jover A, Saquete E. “Here Are the Rules: Ignore All Rules”: Automatic Contradiction Detection in Spanish. Applied Sciences. 2021; 11(7):3060. https://doi.org/10.3390/app11073060
URI: http://hdl.handle.net/10045/113915
DOI: 10.3390/app11073060
ISSN: 2076-3417
Abstract: 
This paper tackles automatic detection of contradictions in Spanish within the news domain. Two pieces of information are classified as compatible, contradictory, or unrelated information. To deal with the task, the ES-Contradiction dataset was created. This dataset contains a balanced number of each of the three types of information. The novelty of the research is the fine-grained annotation of the different types of contradictions in the dataset. Presently, four different types of contradictions are covered in the contradiction examples: negation, antonyms, numerical, and structural. However, future work will extend the dataset with all possible types of contradictions. In order to validate the effectiveness of the dataset, a pretrained model is used (BETO), and after performing different experiments, the system is able to detect contradiction with a F1m of 92.47%. Regarding the type of contradictions, the best results are obtained with negation contradiction (F1m = 98%), whereas structural contradictions obtain the lowest results (F1m = 69%) because of the smaller number of structural examples, due to the complexity of generating them. When dealing with a more generalistic dataset such as XNLI, our dataset fails to detect most of the contradictions properly, as the size of both datasets are very different and our dataset only covers four types of contradiction. However, using the classification of the contradictions leads us to conclude that there are highly complex contradictions that will need external knowledge in order to be properly detected and this will avoid the need for them to be previously exposed to the system.
Keywords:Contradiction detection, Natural language processing, Deep learning, Human language technologies
MDPI
info:eu-repo/semantics/article