Action in cognition: the case of language

Empirical research has shown that the processing of words and sentences is accompanied by activation of the brain's motor system in language users. The degree of precision observed in this activation seems to be contingent upon (1) the meaning of a linguistic construction and (2) the depth with which readers process that construction. In addition, neurological evidence shows a correspondence between a disruption in the neural correlates of overt action and the disruption of semantic processing of language about action. These converging lines of evidence can be taken to support the hypotheses that motor processes (1) are recruited to understand language that focuses on actions and (2) contribute a unique element to conceptual representation. This article explores the role of this motor recruitment in language comprehension. It concludes that extant findings are consistent with the theorized existence of multimodal, embodied representations of the referents of words and the meaning carried by language. Further, an integrative conceptualization of “fault tolerant comprehension” is proposed

Orality and literacy: epic heroes of human destiny?

Human Speech has often been presented as the crucial line dividing humanity from animals, with Literacy then entering in as the fulfiller of human destiny, the redeemer from primitive orality, and, in alphabetic apotheosis, the all-conquering hero of the west's civilising mission. This epic tale, pervasive as it still remains, is now under attack from many directions, not least in the expanding work on multiple literacy practices. This paper takes the complementary line of starting from the 'oral' end of the apparent equation. Building on anthropological fieldwork, experience of teaching at the UK's Open University, and the gathering comparative literature, it questions the limiting linguistic bias of this tale – itself a kind of myth explaining and justifying the social order. Verbal language is just one of many modes and media of human communication and can only be fully understood in conjunction with them. The traditional myth also underplays the multiplex nature of language itself: researchers across several disciplines are now increasingly revealing the multimodal nature of human speech and writing. To focus on narrower definitions of language and, as in the heroic elevation of 'orality' and 'literacy', to propose the treatment of language as the crucial factor in human culture is to proffer a thin and single-line tale of human history and, with it, a misleading and ethnocentric account not only of humanity more generally but also of many of our actual educational practices. It needs to be replaced by a broader, more cross-cultural and more generous model of both learning and communication

Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project “HOLOTRAIN” (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project “AWAKEN: content-Aware and netWork-Aware faKE News mitigation” (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project “Deep-Learning Anomaly Detection for Human and Automated Users Behavior” (grant no. 91809358)

Formalizing Multimedia Recommendation through Multimodal Deep Learning

Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains. Multimodality can help tap into richer information sources and construct more refined user/item profiles for recommendations. However, existing literature lacks a shared and universal schema for modeling and solving the recommendation problem through the lens of multimodality. This work aims to formalize a general multimodal schema for multimedia recommendation. It provides a comprehensive literature review of multimodal approaches for multimedia recommendation from the last eight years, outlines the theoretical foundations of a multimodal pipeline, and demonstrates its rationale by applying it to selected state-of-the-art approaches. The work also conducts a benchmarking analysis of recent algorithms for multimedia recommendation within Elliot, a rigorous framework for evaluating recommender systems. The main aim is to provide guidelines for designing and implementing the next generation of multimodal approaches in multimedia recommendation

A Short Survey on Deep Learning for Multimodal Integration: Applications, Future Perspectives and Challenges

Deep learning has achieved state-of-the-art performances in several research applications nowadays: from computer vision to bioinformatics, from object detection to image generation. In the context of such newly developed deep-learning approaches, we can define the concept of multimodality. The objective of this research field is to implement methodologies which can use several modalities as input features to perform predictions. In this, there is a strong analogy with respect to what happens with human cognition, since we rely on several different senses to make decisions. In this article, we present a short survey on multimodal integration using deep-learning methods. In a first instance, we comprehensively review the concept of multimodality, describing it from a two-dimensional perspective. First, we provide, in fact, a taxonomical description of the multimodality concept. Secondly, we define the second multimodality dimension as the one describing the fusion approaches in multimodal deep learning. Eventually, we describe four applications of multimodal deep learning to the following fields of research: speech recognition, sentiment analysis, forensic applications and image processing