Dialogue Act Dialogue Act TaggingTagging

Using TBLUsing TBL

Sachin KambojSachin Kamboj

CISC 889: Statistical Approaches to NLPCISC 889: Statistical Approaches to NLPSpring 2003Spring 2003

April 19, 2023April 19, 2023

Outline Dialogue Acts Transformation Based Learning (TBL)

Introduction Training Phase Example

Motivation for use of TBL in Dialogue Limitations of TBL Monte Carlo Version of TBL Other Extensions References

Dialogue Acts For a computer system to understand (and

participate in) a dialogue, the system should know the intentions of the speaker.

Defined as: A concise abstraction of the intentional function of a speaker.

# Speaker Utterance Dialogue Act

1 John Hello. Greet

2 John I’d like to meet you on Tuesday at 2:00 Suggest

3 Mary That’s no good for me, Reject

4 Mary But I am free at 3:00 Suggest

5 John That sounds fine to me Accept

6 John I’ll see you then. Bye

Transformation-based Learning

Uses machine learning to generate a sequence of rules to use in tagging data

The first rules are general, making sweeping generalization and several errors

Subsequent rules are more specific and usually correct specific errors

TBL: Training PhaseSTART

For each incorrect tag,generate ALL rules to

fix this tag

Compute a score for each rule

Apply this rule to the corpus

Find the highest Scoring rule

Repeat?

END

NoNo

YesYes

Label each utterancewith an initial tag

TBL: Example

# Condition (s) New Dialogue Act1 None SUGGEST

2 Includes “see” and “you” BYE

3 Includes “sounds” ACCEPT

4 Length < 4 words Previous tag is none GREET

5 Includes “no” Previous tag is SUGGEST REJECT

UtteranceCorrect

TagRule #

1 2 3 4 5

Hello. G S S S G G

I’d like to meet you on Tuesday at 2:00 S S S S S S

That’s no good for me, R S S S S R

But I am free at 3:00 S S S S S S

That sounds fine to me A S S A A A

I’ll see you then. B S B B B B

TBL: Motivation for use in Dialogue Computing Dialogue Acts is similar to

computing POS tags: POS tags depend on surrounding words Dialogue Acts depend on surrounding

utterances. Advantages over other ML methods used for

Dialogue Act Tagging: Generates intuitive rules:

Allows researchers to gain insights into discourse theory

Leverage learning: Can use tags that have already been computed.

Does not overtrain

Limitations TBL does not assign a confidence measure to

the tags TBL is highly dependent on the rule

templates. Rule templates need to be manually selected

in advance However, its difficult to find only and all the

relevant templates. Omissions would result in handicapping the

system Solution: Allow the system to learn which

templates are useful Allow an overly general set of templates TBL is capable of discarding irrelevant rules…

Limitations (cont.) Problem with the solution:

The system becomes intractable with more than 30 templates

The system must generate and evaluate O( ip(v + 1)(2d + 1)f )

rules, where: i = no of instancesp = no of passesf = no of featuresd = max. feature distancev = avg. no. of distinct

values for each feature

A Monte Carlo Version of TBL Allows the system to consider a huge number

of templates while still maintaining tractability System does not perform an exhaustive

search through the space of possible rules. Instead only R randomly selected templates

are instantiated at each pass Hence, system only considers O ( ipR ) rules. System still finds the best rules:

Because the system has many opportunities to find the best rules

Other Extensions

Committee-Based Sampling method to assign confidence measures

Automatic generation of “Cue Phases”

Reference Sources Samuel, Ken and Carberry, Sandra and Vijay-Shanker, K. 1998.

Dialogue Act Tagging with Transformation-Based Learning. In Proceedings of the 17th International Conference on Computational Linguistics and the 36th Annual Meeting of the Association for Computational Linguistics. Montreal, Quebec, Canada. 1150-1156.

Samuel, Ken and Carberry, Sandra and Vijay-Shanker, K. 1998. An Investigation of Transformation-Based Learning in Discourse. In Machine Learning: Proceedings of the Fifteenth International Conference. Madison, Wisconsin. 497-505.

Samuel, Ken and Carberry, Sandra and Vijay-Shanker, K. 1999. Automatically Selecting Useful Phrases for Dialogue Act Tagging. In Proceedings of the Fourth Conference of the Pacific Association for Computational Linguistics. Waterloo, Ontario, Canada.

Samuel, Ken and Carberry, Sandra and Vijay-Shanker, K. 1998. Computing Dialogue Acts from Features with Transformation-Based Learning. In Applying Machine Learning to Discourse Processing: Papers from the 1998 AAAI Spring Symposium. Stanford, California. 90-97.

Questions?