Neural Machine Translation (NMT) performs training of a neural network employing an encoder-decoder architecture. However, the quality of the neural-based translations predominantly depends on the availability of a large amount of bilingual training dataset. In this paper, we explore the performance of translations predicted by attention-based NMT systems for Spanish to Persian low-resource language pairs. We analyze the errors of NMT systems that occur in the Persian language and provide an in-depth comparison of the performance of the system based on variations in sentence length and size of the training dataset. We evaluate our translation results using BLEU and human evaluation measures based on the adequacy, fluency, and overall rating.

Cybersecurity experts rely on the knowledge stored in databases like the NVD to do their work, but these are not the only sources of information about threats and vulnerabilities. Much of that information flows through social media channels. In this paper we argue that security experts and general users alike can benefit from the technologies of the Semantic Web, merging heterogeneous sources of knowledge in an ontological representation. We present a system that has an ontology of vulnerabilities at its core, but that is enhanced with NLP tools to identify cybersecurity-related information in social media and to launch queries over heterogeneous data sources. The transformative power of Semantic Web technologies for cybersecurity, which has been proven in the biomedical field, is evaluated and discussed.

Modern software development is increasingly collaborative. Open Source Software (OSS) are the bellwether; they support dynamic teams, with tools for code sharing, communication, and issue tracking. The success of an OSS project is reliant on team communication. E.g., in issue discussions, individuals rely on rhetoric to argue their position, but also maintain technical relevancy. Rhetoric and technical language are on opposite ends of a language complexity spectrum: the former is stylistically natural; the latter is terse and concise. Issue discussions embody this duality, as developers use rhetoric to describe technical issues. The style mix in any discussion can define group culture and affect performance, e.g., issue resolution times may be longer if discussion is imprecise. Using GitHub, we studied issue discussions to understand whether project-specific language differences exist, and to what extent users conform to a language norm. We built project-specific and overall GitHub language models to study the effect of perceived language complexity on multiple responses. We find that experienced users conform to project-specific language norms, popular individuals use overall GitHub language rather than project-specific language, and conformance to project-specific language norms reduces issue resolution times. We also provide a tool to calculate project-specific perceived language complexity.