Dialogue authoring in large games requires not only content creation but the subtlety of its delivery, which can vary from character to character. Manually authoring 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 generation engine for realizing it, and expands upon a translation tool that bridges the two. We add functionality to the translator to allow direct speech to be modeled by the narrative representation, whereas the original translator supports only narratives told by a third person narrator. We show that we can perform character substitution in dialogues. We implement and evaluate a potential application to dialogue implementation: generating dialogue for games with big, dynamic, or procedurally-generated open worlds. We present a pilot study on human perceptions of the personalities of characters using direct speech, assuming unknown personality types at the time of authoring.
According to observations, children with autism frequently speak more slowly than similarly developing kids. They differ in their speech in other ways, most notably in tone, intonation, and rhythm. It is very challenging to consistently and objectively describe these "prosodic" distinctions, and it has been decades since their roots have been identified. Researchers from Northwestern University and Hong Kong collaborated on a study to shed light on the causes and diagnoses of this illness. This method uses machine learning to find speech patterns in autistic children that are similar in Cantonese and English.
There are as many as 1.5 billion English speaking people in the world, including those who speak English as a second language. That may sound like a lot, but that means four out of every five people do not speak English. Therefore, any speech recognition or natural language technology that is built primarily for English speakers will be missing out on 5.9 billion potential customers. That is a big opportunity; but with 6,500 spoken languages still in use throughout the world, it is also a very big challenge. Speech technology has solid roots in American research.
In this demo we present a visualization of formalized representations of story. Introducing the interactive to storytelling requires the management of experiences that a user creates by their decisions. These sorts of variations can have impact on not only the user, but also the retrievable content appropriate to present to the user. The overall contribution of this work is to identify the player impact of story variation by modeling supplementary variations, and systematically responding to player interaction through supplementary variation, while respecting the author's intentions by maintaining the integrity of the core story skeleton.
A set of carefully-worded warnings directed to the right accounts could help reduce the amount of hate on Twitter. That's the conclusion of new research examining whether targeted warnings could reduce hate speech on the platform. Researchers at New York University's Center for Social Media and Politics found that personalized warnings alerting Twitter users to the consequences of their behavior reduced the number of tweets with hateful language a week after. While more study is needed, the experiment suggests that there is a "potential path forward for platforms seeking to reduce the use of hateful language by users," according to Mustafa Mikdat Yildirim, the lead author of the paper. In the experiment, researchers identified accounts at risk of being suspended for breaking Twitter's rules against hate speech.