NLP

Procedural story generation is an active and increasingly important area of interactive entertainment. Some of the most difficult challenges with the concept are generation and managing of dramatic elements with high degree of variation that are both internally consistent and entertaining. If an authoring system could access a large repository of proven sets of domain specific dramatic action sequences with guaranteed internal structure, consistency and known entertainment value, could it generate unique stories with the same drama in a different domain?

Automated paraphrasing of natural language text has many interesting applications from aiding in better translations to generating better and more appropriate style language. In this paper, we are concerned with the problem of picking the best English sentence out of a set of machine generated paraphrase sentences, each designed to express the same content as a human generated original. We present a system of scoring sentences based on examples in large corpora.

Currently, stylistic analysis of natural language texts is achieved through a wide variety of techniques containing many different algorithms, feature sets and collection methods. Most machine-learning methods rely on feature extraction to model the text and perform classification. But what are the best features for making style based distinctions? While many researchers have developed particular collections of style features – called style markers – no definitive list exists.

Style transformation refers to the process by which a piece of text written in a certain style of writing is transformed into another text exhibiting a distinctly different style of writing without significant change to the meaning of individual sentences. In this paper we continue investigation into the linguistic style transformation problem and demonstrate current achievements in transformation on sample texts from a standard authorship attribution corpus.

Generating believable and contextual dialogue among non-playercharacters (NPC) remains one of the major challenges in interactive entertainment. Dialogue scenes in virtual environments are crucial to narrative progression and user believability, yet they continue to demand heavy authorial burden. In this paper, we describe our project Grapevine, a system for generating gossipstyle conversation. We model the gossip conversation with a series of speech-acts controlled by a dialogue manager. We model characters with traits derived from the Big Five theory of personality.