Adaptive Spoken Dialogue Systems & Computational

Linguistics

Diane J. Litman

Dept. of Computer Science & Learning Research and Development Center

University of PittsburghPittsburgh, PA 15260 USA

http://www.cs.pitt.edu/~litman\

Spoken Dialogue Systems

• Front end: ASR (automatic speech recognition) and TTS (text to speech)

• Back end: DB, web, tutor, chat room, etc.• Middle: dialogue strategy (what action to

take at each point)

user

ASR

TTS

backendspoken dialogue system

Overview of Issues

Problem: building a spoken dialogue system is currently more of an art than a science

• design choices strongly influence performance• most choices made by human intuition

Hypothesis: performance can be improved by

• learning how to make optimal choices• dynamic system adaptation when problems do

occur

Why / When / How to Adapt

• Many types of problems– Task failure, poor speech recognition, poor

natural language processing, user emotional state

• Many types of clues– Words, intonation, timing, emotion, logged

information

• Many forms of adaptation– machine learning vs. user-led– What kind of learning algorithms?

• Many levels of granularity– dialogue vs. subdialogue vs. utterance

RLDS: Adaptive System I• Optimization of dialogue strategy via reinforcement learning• Parameters: linguistic state (to monitor), reward (to optimize)• Example Result: task success increases from 51.5% to 63.5%

TTS: Welcome to RLDS. Please say an activity name or say 'list activities' for a list of activities I know about. USER: I would like to visit a historic site in Stanhope this morning. TTS: Did you say you are interested in going to a zoo? USER: No. TTS: I know about amusement parks, aquariums, cruises, historic sites, museums, parks, theaters, wineries, and zoos. Please say an activity name from this list. USER: I would like to visit a historic site. TTS: Did you say you are interested in going to a historic site? USER: Yes. TTS: Please give me more information. USER: ... TTS: Please tell me the location that you are interested in. You can also tell me the time.…. (8 turns omitted)TTS: Thank you for using the system. Please give me feedback by saying 'good', 'so-so', or 'bad'. USER: Bad. TTS: Goodbye and have a nice day!

TTS: Welcome to RLDS. How may I help you? USER: I would like to um find about Stanhope historic sites. TTS: Did you say you are interested in Stanhope? USER: Yes. TTS: What time of the day do you want to go? USER: Uh...morning. TTS: Did you say you want to go in the morning? USER: Yes. TTS: I found 2 historic sites in Stanhope that are open in the morning. They are the Lenape Indian Village, and the Waterloo Village. Thank you for using the system. Please give me feedback by saying 'good', 'so-so', or 'bad'. USER: Good. TTS: Goodbye and have a nice day!

Unoptimized System

Optimized System

TOOT: Adaptive System II

• Dynamic system adaptation to particular users within particular dialogues via supervised learning

• Monitoring of subdialogues for ASR performance increases task success from 23% to 65%

• Monitoring of prosodic, lexical, and contextual features accurately predicts user error detection and recovery utterances

A Problematic DialogueS: Hi, this is AT&T Amtrak schedule system. This is TOOT. How may

I help you?U: I'd like to find a train from Ch- to Chicago from Baltimore.

MISRECOGNITIONS: Sorry, I can't understand you. Please repeat your utterance. U: I'd like to find a train from Baltimore to Chicago.

MISRECOGNITIONS: I heard you say “leave today around 5 thirty in the morning ."Which city do you want to go to? U: I would like to leave Saturday at eight in the morning.

MISRECOGNITIONS: You can say the arrival city name, such as “New York City."U: Baltimore to Chicago.…

A Successful Dialogue after Adaptation

S: Hi, this is AT&T Amtrak schedule system. This is Toot. How may I help you?

U: I need to find a train going to New York City .S: Which city do you want to leave from? U: Baltimore.S: Do you want me to find the trains from Baltimore to DC on

Thursday at anytime now?U: No. S: How may I help you? U: I need to leave on Sunday.**TOOT adapts to more conservative dialogue behaviors**S: I heard you say go to DC between 1 night and 8. Do you want me to

find the trains from Baltimore to DC on Thursday between 1 night and 8 now?

U: No.

S: You can say yes or no.

U: No.

S: How may I help you?

U: I need to go to New York City from Baltimore on Sunday at 8:30 pm.

S: I heard you say go to Chicago on Tuesday around 8:45 am. Do you want me to find the trains from Baltimore to Chicago on Tuesday around 8:45 am now?

U: No.

**TOOT adapts to even more conservative dialogue behaviors**

S: Which city do you want to go to?

U: New York City.

S: Do you want to go to New York City?

U: Yes.

S: Which city do you want to leave from?

U: Baltimore...

ITSPOKE: Adaptive System IIIAdaptation of intelligent tutoring system’s pedagogical dialogue

strategies to student attitudes and emotions

Tutor26: Let me ask my question in a more general way. What happens to the velocity of a body when there is no force acting on it?

Student27: dammit (ASR: it is)Tutor28 : Could you please repeat that?Student29 :same (ASR: i same)Tutor30 : Right. And what happens to the HORIZONTAL velocity of a body when

there is no HORIZONTAL force acting on it?Student31: zero (ASR: the zero)Tutor32 : Well... OK, let's consider how forces in the vertical direction affect the

motion of a body in a horizontal direction in a different scenario <…omitted…>Student33: oh gravity you already said this (ASR: beats gravity you're exert less)Tutor34: Fine. Are there any other forces acting on the apple as it falls?Student35: no why are you doing this again (ASR: no y and to it yes)Tutor36: Correct. The only force acting on the apple as it falls to the earth is the

earth's gravitational force. In which direction does gravitational force act?Student37: downward you computer (ASR: downward you computer)

Summary

• Adaptation/Personalization via Learning can improve dialogue system performance

• RLDS: Given exploratory dialogues, reinforcement learning can be used for online system optimization

• TOOT: By learning from training dialogues, a system can dynamically look for problems and adapt

• ITSPOKE, Your System here: New types of voice-enabled systems pose many interesting opportunities