Ø  Cloud-based system: webpage version Ø  Policy trained with different sets of focused units:

-  All units considered (no specific focused unit) -  Retroflex only (four special Mandarin units)

•  ㄓ(j), ㄔ(ch), ㄕ(sh), ㄖ(r) Ø Demo scenario:

Here we test on Retroflex-specific learners, whether system provide unit practice in needed

NTU Chinese 2.0: A Personalized Recursive Dialogue Game for Computer-Assisted Learning of Mandarin Chinese

Pei-hao Su, Tien-han Yu, Ya-Yuun Su, and Lin-shan Lee National Taiwan University

Ref: [1] http://chinese.ntu.edu.tw/

1. Tree-structured turn-taking dialogue script

. .

1

2

9

•  Nine sub-dialogues in restaurant scenario •  Each turn contains several sentence choices

2. Markov Decision Process •  State: (high-dimensional continuous space)

•  Action: Sentences to be selected

-  Present dialogue turn -  Avg. scores of each unit so far

•  Reward: -  Cost -1 for every state transition (dialogue turn) -  Game ends when system goal reached

•  Policy Training: Fitted Value Iteration with regularization 3. Learner Simulation

GMM

Sample Simulated Learners

88.6 63.7 … …

Real Learner

/b/ /p/ ... /a/ /i/ /u/.. Simulated scores (PSV) Train Model

Current    

Previous  

Detail  Analysis  of  produced  sentence

Selected  by  system  policy

Summary

System Framework

Technical Details

Demonstration

MDP

Automatic Pronunciation

Evaluator

Learner

Selected Sentences (Action)

Utterance Input

Training Data Learner Simulation Model

(State) Dialogue Manager

ü Personalization: System policy on Markov Decision Process providing learning materials with more practice on Poorly pronounced units

Ø A dialogue game system with pronunciation feedback over personalized learning materials ü Feedback: Automatic pronunciation evaluation by NTU

Chinese [1], scores all pronunciation units in an utterance

Ø More interesting: Repeated practice of the same units in different sentences, no need to repeat the same sentences

ü System Goal: Learner’s selected focused pronunciation units over the recursive dialogue script have scores exceeding 75 over 7 times in min num. of turns

(8) Bill paying

(1) Phone

invitation

(7) Chatting 2

(6) Meal serving

(5) Chatting 1

(3) Finding the restaurant

(4) Seating and

meal ordering

(2) Restaurant reservation

(9) Saying

goodbye

Present Performance Statistics

Score  distribu9on  at  each  turn

Score Retroflex Score

Score Retroflex Score Percentage over Random

Extra  prac9ce  over  random  in  past  10  turns  given  score  distribu9on  of  

10  dialogue  turns  before

Ø  Effective practice opportunities on focused units than random in the near future of each turn!

Dia

logu

e tu

rn

1 2 3 4 5 6 7

14 15 16 17

…

..

Ø Any combination of sub-dialogue can be chosen