Thiago A. S. Pardo

Francielle Vargas, Kokil Jaidka, Thiago A. S. Pardo et al.

Automated news credibility and fact-checking at scale require accurately predicting news factuality and media bias. This paper introduces a large sentence-level dataset, titled "FactNews", composed of 6,191 sentences expertly annotated according to factuality and media bias definitions proposed by AllSides. We use FactNews to assess the overall reliability of news sources, by formulating two text classification problems for predicting sentence-level factuality of news reporting and bias of media outlets. Our experiments demonstrate that biased sentences present a higher number of words compared to factual sentences, besides having a predominance of emotions. Hence, the fine-grained analysis of subjectivity and impartiality of news articles provided promising results for predicting the reliability of media outlets. Finally, due to the severity of fake news and political polarization in Brazil, and the lack of research for Portuguese, both dataset and baseline were proposed for Brazilian Portuguese.

Rafael T. Anchieta, Marco A. S. Cabezudo, Thiago A. S. Pardo

Abstract Meaning Representation (AMR) is a recently designed semantic representation language intended to capture the meaning of a sentence, which may be represented as a single-rooted directed acyclic graph with labeled nodes and edges. The automatic evaluation of this structure plays an important role in the development of better systems, as well as for semantic annotation. Despite there is one available metric, smatch, it has some drawbacks. For instance, smatch creates a self-relation on the root of the graph, has weights for different error types, and does not take into account the dependence of the elements in the AMR structure. With these drawbacks, smatch masks several problems of the AMR parsers and distorts the evaluation of the AMRs. In view of this, in this paper, we introduce an extended metric to evaluate AMR parsers, which deals with the drawbacks of the smatch metric. Finally, we compare both metrics, using four well-known AMR parsers, and we argue that our metric is more refined, robust, fairer, and faster than smatch.