Title :

Online learning and transfer for user adaptation in dialogue systems

Author(s) :

Carrara, Nicolas [Auteur]
Sequential Learning [SEQUEL]
Orange Labs [Lannion]
Laroche, Romain [Auteur]
Maluuba
Pietquin, Olivier [Auteur]
Sequential Learning [SEQUEL]

Conference title :

SIGDIAL/SEMDIAL joint special session on negotiation dialog 2017

City :

Saarbrücken

Country :

Allemagne

Start date of the conference :

2017-08-15

HAL domain(s) :

Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Informatique et langage [cs.CL]

English abstract : [en]

We address the problem of user adaptation in Spoken Dialogue Systems. The goal is to quickly adapt online to a new user given a large amount of dialogues collected with other users. Previous works using Transfer for ...
Show more >We address the problem of user adaptation in Spoken Dialogue Systems. The goal is to quickly adapt online to a new user given a large amount of dialogues collected with other users. Previous works using Transfer for Reinforcement Learning tackled this problem when the number of source users remains limited. In this paper, we overcome this constraint by clustering the source users: each user cluster, represented by its centroid, is used as a potential source in the state-of-the-art Transfer Reinforcement Learning algorithm. Our benchmark compares several clustering approaches , including one based on a novel metric. All experiments are led on a negotiation dialogue task, and their results show significant improvements over baselines.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL