Search Results for author:
Found 6 papers, 4 papers with code
Data Collection for Interactive Learning through the Dialog
This paper presents a dataset collected from natural dialogs which enables to test the ability of dialog systems to learn new facts from user utterances throughout the dialog.
Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings
We present a natural language generator based on the sequence-to-sequence approach that can be trained to produce natural language strings as well as deep syntax dependency trees from input dialogue acts, and we use it to directly compare two-step generation with separate sentence planning and surface realization stages to a joint, one-step approach.
Recurrent Neural Networks for Dialogue State Tracking
This paper discusses models for dialogue state tracking using recurrent neural networks (RNN).
A Context-aware Natural Language Generator for Dialogue Systems
We present a novel natural language generation system for spoken dialogue systems capable of entraining (adapting) to users' way of speaking, providing contextually appropriate responses.
Denotation Extraction for Interactive Learning in Dialogue Systems
This paper presents a novel task using real user data obtained in human-machine conversation.
Neural Generation for Czech: Data and Baselines
We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach.
