Multimodal Grounding for Language Processing

This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference of Computational Linguistics. Please refer to this version for citations: https://www.aclweb.org/anthology/papers/C/C18/C18-1197

Strategy paradigms for the management of quality:dealing with complexity

Quality management is dominated by rational paradigms for the measurement and management of quality, but these paradigms start to “break down”, when faced with the inherent complexity of managing quality in intensely competitive changing environments. In this article, the various theoretical strategy paradigms employed to manage quality are reviewed and the advantages and limitations of these paradigms are highlighted. A major implication of this review is that when faced with complexity, an ideological stance to any single strategy paradigm for the management of quality is ineffective. A case study is used to demonstrate the need for an integrative multi-paradigm approach to the management of quality as complexity increases

Uni- and Multimodal and Structured Representations for Modeling Frame Semantics

Language is the most complex kind of shared knowledge evolved by humankind and it is the foundation of communication between humans. At the same time, one of the most challenging problems in Artificial Intelligence is to grasp the meaning conveyed by language. Humans use language to communicate knowledge and information about the world and to exchange their thoughts. In order to understand the meaning of words in a sentence, single words are interpreted in the context of the sentence and of the situation together with a large background of commonsense knowledge and experience in the world. The research field of Natural Language Processing aims at automatically understanding language as humans do naturally. In this thesis, the overall challenge of understanding meaning in language by capturing world knowledge is examined from the two branches of (a) knowledge about situations and actions as expressed in texts and (b) structured relational knowledge as stored in knowledge bases. Both branches can be studied with different kinds of vector representations, so-called embeddings, for operationalizing different aspects of knowledge: textual, structured, and visual or multimodal embeddings. This poses the challenge of determining the suitability of different embeddings for automatic language understanding with respect to the two branches. To approach these challenges, we choose to closely rely upon the lexical-semantic knowledge base FrameNet. It addresses both branches of capturing world knowledge whilst taking into account the linguistic theory of frame semantics which orients on human language understanding. FrameNet provides frames, which are categories for knowledge of meaning, and frame-to-frame relations, which are structured meta-knowledge of interactions between frames. These frames and relations are central to the tasks of Frame Identification and Frame-to-Frame Relation Prediction. Concerning branch (a), the task of Frame Identification was introduced to advance the understanding of context knowledge about situations, actions and participants. The task is to label predicates with frames in order to identify the meaning of the predicate in the context of the sentence. We use textual embeddings to model the semantics of words in the sentential context and develop a state-of-the-art system for Frame Identification. Our Frame Identification system can be used to automatically annotate frames on English or German texts. Furthermore, in our multimodal approach to Frame Identification, we combine textual embeddings for words with visual embeddings for entities depicted on images. We find that visual information is especially useful in difficult settings with rare frames. To further advance the performance of the multimodal approach, we suggest to develop embeddings for verbs specifically that incorporate multimodal information. Concerning branch (b), we introduce the task of Frame-to-Frame Relation Prediction to advance the understanding of relational knowledge of interactions between frames. The task is to label connections between frames with relations in order to complete the meta-knowledge stored in FrameNet. We train textual and structured embeddings for frames and explore the limitations of textual frame embeddings with respect to recovering relations between frames. Moreover, we contrast textual frame embeddings versus structured frame embeddings and develop the first system for Frame-to-Frame Relation Prediction. We find that textual and structured frame embeddings differ with respect to predicting relations; thus when applied as features in the context of further tasks, they can provide different kinds of frame knowledge. Our structured prediction system can be used to generate recommendations for annotations with relations. To further advance the performance of Frame-to-Frame Relation Prediction and also of the induction of new frames and relations, we suggest to develop approaches that incorporate visual information. The two kinds of frame knowledge from both branches, our Frame Identification system and our pre-trained frame embeddings, are combined in an extrinsic evaluation in the context of higher-level applications. Across these applications, we see a trend that frame knowledge is particularly beneficial in ambiguous and short sentences. Taken together, in this thesis, we approach semantic language understanding from the two branches of knowledge about situations and actions and structured relational knowledge and investigate different embeddings for textual, structured and multimodal language understanding

Motion to innovation: Brand value sources have (not) changed over time

Innovation drives the expansion of economies in a global dimension. This is also the reason why contemporary researches indicates the trend of incorporation of the innovation to the strategic concept of brand value building and managing. It has been proven, that innovation is relevant source of brand value perceived by consumers. Such a trend has been established in reaction to the growing importance of brand value for competitive advantage creation in a global perspective. Currently published scientific contributions mainly highlight the importance of brand innovation in selected sectors of national economies abstracting from innovation perceptions due to the national socio-psychological profile. This is the reason why universally applicable theory of innovation in scope of brand value building and managing is missing. The lack of the theory can be removed by identification of importance of innovation attributes as brand value sources in context of market specifics. So, the aim of this paper is to provide such an identification and to verify existence of divergences between “foreign” theory and “domestic” practice. To do that, we use questionnaire, selection analysis and cluster analysis. We detect specifics of brand value perception focusing on innovation and its attributes comparing theory and reality of Slovak environment

Laddered motivations of external whistleblowers: The truth about attributes, consequences, and values

The purpose of this study was to explore the motivational structures of external whistleblowers involved in the decision to blow the whistle by applying MEC theory and the laddering technique. Using both soft and hard laddering methods, data were collected from 37 Korean external whistleblowers. Results revealed that the means-end chain of external whistleblow-ers was the hierarchical linkage among two concrete attributes (the power of external whistleblowing to make changes and its warning about the seriousness of wrongdoing to the public), two functional consequences (correcting a wrongdoing and making those who violated laws admit their offenses), and one terminal value (the truth). The extant whistleblowing literature has either made assumptions about whistleblowers’ motivations when developing models or has drawn indirect inferences from measures of other variables. Our study is the first with an explicit and empirical focus on whistleblowers’ motivations. The findings provide evidence of the motivational structures of external whistleblowers that consist of a set of complex paths linked by multi-layered motivators. This research will be helpful in designing and reviewing whistleblowing programs for organizations, regulatory agencies, and journalists