Computational visual storytelling produces a textual description of events and interpretations depicted in a sequence of images. These texts are made possible by advances and cross-disciplinary approaches in natural language processing, generation, and computer vision. We define a computational creative visual storytelling as one with the ability to alter the telling of a story along three aspects: to speak about different environments, to produce variations based on narrative goals, and to adapt the narrative to the audience. These aspects of creative storytelling and their effect on the narrative have yet to be explored in visual storytelling. This paper presents a pipeline of task-modules, Object Identification, Single-Image Inferencing, and Multi-Image Narration, that serve as a preliminary design for building a creative visual storyteller. We have piloted this design for a sequence of images in an annotation task. We present and analyze the collected corpus and describe plans towards automation.

Progress in the Machine Translation (MT) research community, particularly for statistical approaches, is intensely data-driven. Acquiring source language documents for testing, creating training datasets for customized MT lexicons, and building parallel corpora for MT evaluation require translators and non-native speaking analysts to handle large document collections. These collections are further complicated by differences in format, encoding, source media, and access to metadata describing the documents. Automated tools that allow language professionals to quickly annotate, translate, and evaluate foreign language documents are essential to improving MT quality and efficacy. The purpose of this paper is present our research approach to improving MT through pre-processing source language documents. In particular, we will discuss the development and use of MTriage, an application environment that enables the translator to markup documents with metadata for MT parameterization and routing. The use of MTriage as a web-enabled front end to multiple MT engines has leveraged the capabilities of our human translators for creating lexicons from NFW (Not-Found-Word) lists, writing reference translations, and creating parallel corpora for MT development and evaluation.