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More and more of the information on the web is dialogic, from Facebook newsfeeds, to forum conversations, to comment threads on news articles. In contrast to traditional, mono-logic Natural Language Processing resources such as news, highly social dialogue is frequent in social media, making it a challenging context for NLP. This paper tests a(More)
In order to tell stories in different voices for different audiences , interactive story systems require: (1) a semantic representation of story structure, and (2) the ability to automatically generate story and dialogue from this semantic representation using some form of Natural Language Generation (nlg). However, there has been limited research on(More)
Dialogue authoring in large games requires not only content creation but the subtlety of its delivery, which can vary from character to character. Manually author-ing this dialogue can be tedious, time-consuming, or even altogether infeasible. This paper utilizes a rich narrative representation for modeling dialogue and an expressive natural language(More)
There has been a recent explosion in applications for dialogue interaction ranging from direction-giving and tourist information to interactive story systems. Yet the natural language generation (NLG) component for many of these systems remains largely handcrafted. This limitation greatly restricts the range of applications ; it also means that it is(More)
We present a new corpus, PersonaBank, consisting of 108 personal stories from weblogs that have been annotated with their STORY INTENTION GRAPHS, a deep representation of the fabula of a story. We describe the topics of the stories and the basis of the STORY INTENTION GRAPH representation, as well as the process of annotating the stories to produce the(More)
In this paper, we explore a novel approach to end-to-end round-trip time (RTT) estimation using a machine-learning technique known as the experts framework. In our proposal, each of several 'experts' guesses a fixed value. The weighted average of these guesses estimates the RTT, with the weights updated after every RTT measurement based on the difference(More)
The language used in online forums differs in many ways from that of traditional language resources such as news. One difference is the use and frequency of nonliteral, subjective dialogue acts such as sarcasm. Whether the aim is to develop a theory of sarcasm in dialogue, or engineer automatic methods for reliably detecting sarcasm, a major challenge is(More)
In this paper, we explore a novel approach to end-to-end round-trip time (RTT) estimation using a machine-learning technique known as the Experts Framework. In our proposal, each of several ``experts'' guesses a fixed value. The weighted average of these guesses estimates the RTT, with the weights updated after every RTT measurement based on the difference(More)