Human-Centric Interfaces for Ambient Intelligence

a 3-dimensio nal space is explored rather than a 2-dimensional space. Note that no

path connects to nodes on other planes. Thus, the inference of triangular-chain CRFs

straightforwardly uses jZj-way parallel inferences for linear-chain CRFs. There fore,

for a value of z = k, we perform the inference on the k space and repeat this proce-

dure for all values of z using a modified Viterbi decoding algorithm to find the best

path in the parallel search spaces.

To decrease the time com plexity, the pruning method is employed to reduce the

space of Z by removing planes with p

l

(z|x) < e [15]. We easily calculate p

l

(z|x).

Because Z(z, x) should be calculated before pruning is performed, a recursions are

finished fir st. Thus, we apply the pr uning method only during training. If we wish to

predict the best labels without marginal probabilities in predicting a new instance,

we can omit calculation of Z(x); hence we cannot prune less confident planes. How-

ever, we can take advantage of the pruning method in some applications that require

marginal probabilities (e.g., active learning). The pruning technique reduces training

time, allowing triangul ar-chain CRFs to be scaled for large-scale problems.

8.6 JOINT PREDICTION OF DIALOGUE ACTS AND NAMED ENTITIES

Current state-of-the-art SLU systems use cascade or pipeline schemes [10], which are

often derived by training the NE model; the schemes then use the model’s predic-

tion as a feature for the DA classifier. The NE plays an important role in identifying

the DA; it can thus also improve the performance of the DA classifier in cascading

scheme. However, the cascade approach has a significant drawback: The NE recog-

nition module cannot take advantage of information from the DA identification mod-

ule. Our assumption here is that the problems of modeling DA and modeling NE are

significantly correlated; that is, DA information influences the NE recognition task

and vice versa. Thus, we need to concurrently optimize the DA and NE models. This

problem can be solved using a complex model to reflect inter-dependency between

the two. The triangular-chain CRFs are thus applied to the joint SLU task.

This section evaluates triangular-chain CRFs in a real-world dialogue application

for ambient intelligence: an automated call center. Our method is found to perform

better for SLU tasks since it effectively captures the dependencies between NEs and

DAs. Empirical results show an improvement in both DA classification and NE

recognition.

8.6.1 Data Sets and Experiment Setup

We evaluated our method on two goal-or iented dialogue data Sets: Air-Travel

(English; travel agency service—DARPA-Communicator) and Telebank (Korean;

automated response system for a banking service). All data Sets were collected

and anno tated to develop spoken dialogue systems that consist of annotated DAs

and NEs. In practice, a realistic dialogue system for human–computer interfaces

functions best if the dialogue is short and restrictive. In these data Sets, the average

206 CHAPTER 8 Machine Learning Approaches