Titlebook: Increasing Naturalness and Flexibility in Spoken Dialogue Interaction; 10th International W Erik Marchi,Sabato Marco Siniscalchi,Haizhou Li

CLAY

流浪者

lethargy

Predicting Laughter Relevance Spaces in Dialoguein dialogue and address it with various deep learning models, namely recurrent neural network (RNN), convolution neural network (CNN) and combinations of these. We also attempt to evaluate human performance for this task via an Amazon Mechanical Turk (AMT) experiment. The main finding of the present

fastness

Transfer Learning for Unseen Slots in End-to-End Dialogue State Trackingwhich has not yet been discussed in the literature on conventional approaches. The goal of transfer learning is to improve DST performance for new slots by leveraging slot-independent parameters extracted from DST models for existing slots. An end-to-end DST model is composed of a spoken language un

情感

大漩涡

适宜

混沌

Fantasy

A Classification-Based Approach to Automating Human-Robot Dialogue The classifier was trained on a small corpus of multi-floor Wizard-of-Oz dialogue including two wizards: one standing in for dialogue capabilities and another for navigation. Below, we describe the implementation details of the classifier and show how it was used to automate the dialogue wizard. We

OVERT