The Development of Multimodal Lexical Resources
Human communication is a multimodal activity, involving not only speech and written expressions, but intonation, images, gestures, visual clues, and the interpretation of actions through perception. In this paper, we describe the design of a multimodal lexicon that is able to accommodate the diverse modalities that present themselves in NLP applications. We have been developing a multimodal semantic representation, VoxML, that integrates the encoding of semantic, visual, gestural, and action based features associated with linguistic expressions. 1 Motivation and Introduction The primary focus of lexical resource development in computational linguistics has traditionally been on the syntactic and semantic encoding of word forms for monolingual and multilingual language applications. Recently, however, several factors have motivated researchers to look more closely at the relationship between both spoken and written language and the expression of meaning through other modalities. Specifically, there are at least three areas of CL research that have emerged as requiring significant cross modal or multimodal lexical resource support. These are: Language visualization and simulation generation: Creating images from linguistic input; generating dynamic narratives in simulation environments from action oriented expressions;(Chang et al. 2015; Coyne and Sproat, 2001; Siskind, 2001; Pustejovsky and Krishnaswamy, 2016; Krishnaswamy and Pustejovsky, 2016) Visual Question Answering and image content interpretation: QA and querying over image datasets, based on the vectors associated with the image, but trained on caption image pairings in the data; (Antol et al. 2015; Chao et al. 2015a; Chao et al. 2015b) Gesture interpretation: Understanding integrated spoken language with human or avatargenerated gestures; generating gesture in dialogue to supplement linguistic expressions;(Rautaray and Agrawal, 2015; Jacko, 2012; Turk, 2014; Bunt et al. 1998) To meet the demands for a lexical resource that can help drive such diverse applications, we have been pursuing a new approach to modeling the semantics of natural language, Multimodal Semantic Simulations (MSS) This framework assumes both a richer formal model of events and their participants, as well as a modeling language for constructing 3D visualizations of objects and events denoted by natural language expressions. The Dynamic Event Model (DEM) encodes events as programs in a dynamic logic with an operational semantics, while the language VoxML, Visual Object Concept Modeling Language, is being used as the platform for multimodal semantic simulations in the context of human computer communication, as well as for imageand video related content based querying. Prior work in visualization from natural language has largely focused on object placement and orientation in static scenes (Coyne and Sproat, 2001; Siskind, 2001; Chang et al. 2015) In previous work (Pustejovsky and Krishnaswamy, 2014; Pustejovsky, 2013a) we introduced a method for modeling natural language expressions within a 3D simulation environment, Unity. The goal of that work was to