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The study investigates the representation of the vaccine against COVID-19 in “editorial”, “opinion” and “analysis” articles. The study draws on the appraisal model as a linguistic tool to analyze the attitudinal language of the articles, the differences in which have the potential to be the ground for differentiating “opinion”, “editorial”, and “analysis” texts. The selected 30 articles belong to the period from March 2020 to January 2021, when the importance of the vaccine was unprecedented and all political and economic factors moved to the background. The findings indicate no significant differences in the types of evaluations used, though demonstrate some tendencies that constitute grounds for further research in the field: 1) “analysis” texts draw on less evaluative elements than “editorial” and “opinion”; 2) “opinion” texts demonstrate a balance of appreciation and judgment; 3) there is a dominance of judgment in “editorial” texts. The paper demonstrates that all texts judge the vaccines as effective tools and avoid emotional or negative evaluations.

The ways of explication of evidentiality in analytical articles of the English-language press are investigated. 30 articles of newspapers “The Washington Post”, “The Guardian”, “The Independent” for 2021 are analyzed. The classification of evidential values is presented on the basis of two indicators: the source of information and its degree of reliability. Three types of evidentiality have been identified: direct evidentiality (there is a clear indication of the source of information); impersonal evidentiality (lack of an identified source of information); author’s evidentiality (based on the author’s conclusions). The results of the study showed a significant superiority of direct evidentiality (74 %), realized through the precise indication of the source of information and the use of the author’s “we”. It was revealed that impersonal evidentiality is not typical for an analytical article (6 %). This is due to its specific characteristics: the desire for objectivity and transparency of the data provided. It is noted that this type of evidentiality is realized through adverbs, indicating an anonymous source and using collective nouns. It is shown that the author’s evidentiality is found in 20 % of cases and is realized through filler words, main sentences like that means, adverbs, impersonal sentences using verbs of perception, constructions “to be + adjective with a probability value”, modal verbs.

The article dwells on the two main types of the analytical articles - argumentative and reasoning - which are the results of the variations in the analytical block of the genre composition. It is demonstrated that the argumentative analytical article has a fixed structure which unwinds horizontally or vertically and consists of arguments which support and counter the thesis. It is shown that the reasoning analytical article is aimed at answering an open question, which means that there can’t be a stated structure of the analytical block. The typical elements of this type are description of the main aspects of the situation, coverage of the main views on the issue, explanation of the events and their interconnection, consideration of the possible reactions and consequences.

Humans are well-versed in reasoning about the behaviors of physical objects and choosing actions accordingly to accomplish tasks, while it remains a major challenge for AI. To facilitate research addressing this problem, we propose a new testbed that requires an agent to reason about physical scenarios and take an action appropriately. Inspired by the physical knowledge acquired in infancy and the capabilities required for robots to operate in real-world environments, we identify 15 essential physical scenarios. For each scenario, we create a wide variety of distinct task templates, and we ensure all the task templates within the same scenario can be solved by using one specific strategic physical rule. By having such a design, we evaluate two distinct levels of generalization, namely the local generalization and the broad generalization. We conduct an extensive evaluation with human players, learning agents with varying input types and architectures, and heuristic agents with different strategies. Inspired by how human IQ is calculated, we define the physical reasoning quotient (Phy-Q score) that reflects the physical reasoning intelligence of an agent. Our evaluation shows that 1) all agents are far below human performance, and 2) learning agents, even with good local generalization ability, struggle to learn the underlying physical reasoning rules and fail to generalize broadly. We encourage the development of intelligent agents that can reach the human level Phy-Q score. Website: https://github.com/phy-q/benchmark