Spellchecking is one of the most fundamental and widely used search features. Correcting incorrectly spelled user queries not only enhances the user experience but is expected by the user. However, most widely available spellchecking solutions are either lower accuracy than state-of-the-art solutions or too slow to be used for search use cases where latency is a key requirement. Furthermore, most innovative recent architectures focus on English and are not trained in a multilingual fashion and are trained for spell correction in longer text, which is a different paradigm from spell correction for user queries, where context is sparse (most queries are 1-2 words long). Finally, since most enterprises have unique vocabularies such as product names, off-the-shelf spelling solutions fall short of users' needs.

In parallel programming, there is a shift away from the single execution path of sequential programming to situations where non-deterministic operation force consideration of multiple paths of execution. Compared to the substantial computer science education literature on helping students to learn sequential programming, there are fewer studies of the cognitive difficulties that students follow when learning parallel programming. To address this, we created a computer game, Parallel involving concurrent situations. The game is an abstract representation of concurrency problems where players are asked to solve a progression of puzzles involving arrows moving concurrently on tracks. Play does not require coding. The goals of our research were to 1) explore how students acquire skills in the design of solutions with parallelism, and 2) explore how interactive games can substitute or compliment conventional parallel programming courses. Through two user studies of the game (n=7) where students played the game and used a talk-aloud protocol alongside a researcher, three major themes emerged, that of non-determinism where students were able to make the connection of non-deterministic behavior in parallel programming to the game, self-efficacy where students were stating they felt their knowledge of parallel programming increased after playing the game, and expertise where researchers learned that expertise was important to successful connection of the game to parallel programming concepts These findings show that students are beginning to see the connection between the game/s presentation of concurrency to programming concepts such as non-determinism.

In the context of a learning game to teach parallel programming, we describe a procedural content generation (PCG) approach that can be controlled to generate programming puzzles involving a desired set of concepts, and of desired size and "difficulty". Our approach is based on grammars to control the generation of the puzzle structure, and orthographic graph embedding techniques to render it into a two-dimensional grid for our game. The proposed PCG system is designed to work with a player model in order to provide personalized learning experiences. We present an evaluation of the variability of the generated puzzles using several metrics including challenge and solvability as evaluated by a custom-build model checker. Our evaluation shows that this PCG system can generate a large number of varied puzzles but it is still not able to generate puzzles with certain aesthetic and functional qualities found in puzzles generated by human authors.

In this paper we present a survey of two of the main areas of research within the field of computational narrative, namely narrative analysis and generation. We argue that there is a gap between these two lines of work and propose a taxonomy of the computational models of narrative used within each. We outline potential mappings between computational narrative models with the goal of bridging this gap and alleviating the authorial bottleneck problem occurring when authoring content for computational narrative generation systems. Finally we discuss related work in this direction and report on our work-in-progress towards an end-to-end computational narrative system to bridge the gap.

In this paper, we present our method for automatically extracting narrative information of characters and their narrative roles from natural language stories. In our corpus of 15 unannotated folk tales, our Voz system identifies 87% of the characters in the stories and correctly assigns 68% of the character roles. To better understand the sources of error in our system, we present an analytical methodology to study how the error is introduced by different modules and how it propagates through the pipeline. This methodology allows us to identify the bottleneck with the largest impact on the final error, which might be different from the module with the largest individual error in isolation. Our methodology can be applied to a wide variety of similar information extraction pipelines.