Special issue on "Natural language processing: emerging neural approaches and applications"

Nowadays, systems based on artificial intelligence are being developed, leading to impressive achievements in a variety of complex cognitive tasks, matching or even beating humans [1–4]. Natural language processing (NLP) is a field where the use of deep learning (DL) models in the last five years has allowed AI to advance toward human levels in translation and reading comprehension, as well as other real-world NLP applications, such as question answering and conversational systems, information retrieval, sentiment analysis, and recommender systems. However, due to the difficulties associated with natural language understanding and generation, which are human capabilities among the least understood by computer systems from a cognitive perspective, and despite the remarkable success of DL in different NLP tasks, this is still a field of research of increasing interest [5–7]. In order to improve DL methods, current models have been scaled up, but their complexity has grown toward directions assumed by empirical engineering solutions [8–11]. Moreover, they are not applicable to languages without extensive datasets [12], and the lack of explainability inhibits further improvements [13]. This Special Issue highlights the most recent research being carried out in the NLP field to discuss these open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive