In this paper we present the newest version of retico - a python-based incremental dialogue framework to create state-of-the-art spoken dialogue systems and simulations. Retico provides a range of incremental modules that are based on services like Google ASR, Google TTS and Rasa NLU. Incremental networks can be created either in code or with a graphical user interface. In this demo we present three use cases that are implemented in retico: a spoken translation tool that translates speech in real-time, a conversation simulation that models turn-taking and a spoken dialogue restaurant information service.

Conversations over the telephone require timely turn-taking cues that signal the participants when to speak and when to listen. When a two-way transmission delay is introduced into such conversations, the immediate feedback is delayed, and the interactivity of the conversation is impaired. With delayed speech on each side of the transmission, different conversation realities emerge on both ends, which alters the way the participants interact with each other. Simulating conversations can give insights on turn-taking and spoken interactions between humans but can also used for analyzing and even predicting human behavior in conversations. In this paper, we simulate two types of conversations with distinct levels of interactivity. We then introduce three levels of two-way transmission delay between the agents and compare the resulting interaction-patterns with human-to-human dialog from an empirical study. We show how the turn-taking mechanisms modeled for conversations without delay perform in scenarios with delay and identify to which extend the simulation is able to model the delayed turn-taking observed in human conversation.

We present the Weltmodell, a commonsense knowledge base that was automatically generated from aggregated dependency parse fragments gathered from over 3.5 million English language books. We leverage the magnitude and diversity of this dataset to arrive at close to ten million distinct N-ary commonsense facts using techniques from open-domain Information Extraction (IE). Furthermore, we compute a range of measures of association and distributional similarity on this data. We present the results of our efforts using a browsable web demonstrator and publicly release all generated data for use and discussion by the research community. In this paper, we give an overview of our knowledge acquisition method and representation model, and present our web demonstrator.