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Improving Accuracy of Intention-Based Response Classification using Decision Tree



S.A. Ali, N. Sulaiman, A. Mustapha and N. Mustapha
 
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ABSTRACT

This study focused on improving the dialogue act classification to classify a user utterance into a response class using a decision tree approach. Decision tree classifier is tested on 64 mixed-initiative, transaction dialogue corpus in theater domain. The result from the comparative experiment show that decision tree able to achieve 81.95% recognition accuracy in classification better than the 73.9% obtained using Bayesian networks and 71.3% achieved by using Maximum likelihood estimation. This result showed that the performance of decision tree as classifier is well suited for these tasks.

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  How to cite this article:

S.A. Ali, N. Sulaiman, A. Mustapha and N. Mustapha, 2009. Improving Accuracy of Intention-Based Response Classification using Decision Tree. Information Technology Journal, 8: 923-928.

DOI: 10.3923/itj.2009.923.928

URL: https://scialert.net/abstract/?doi=itj.2009.923.928
 

INTRODUCTION

A dialog system is a computer system intended to converse with a human, with a coherent structure deal with interaction management issues such as turn-taking and topic management and with social aspects of communication like greeting and apologizing (Keizer and Bunt, 2007). In this kind of systems, one of the main concerns is the coherency of the response utterances. In contrast to speech recognition systems, where the goal is the correct transcription of the user utterances. This allows ignoring some words, focusing the attention on those which provide useful information for extracting the meaning of the utterance (Castro et al., 2004). This means, in response systems to generate a potential in the dialogue must be able to recognize this response, while maintaining equal semantic content.

Dialogue systems need to perform dialog act classification, in order to understand the role that an utterance plays in the dialog (e.g., a question for information or a request to perform an action) and to generate an appropriate next turn. In recent years, a variety of empirical techniques have been used to train the dialogue act classifier (Reithinger and Maier, 1995; Stolcke et al., 2000; Walker et al., 2001).

Yang et al. (2008) trained a decision tree classifier using prosodic features to show that the use of dialogue act tagging and prosodic information can help to improve the identification of action item descriptions and agreements. In addition to these features such as confidence score of action motivators and prosody, can be automatically extracted without costly human labeling.

Fodor (2007) presented the basis of a dialogue management communication mechanism that supports decision processes based on decision tree. Where learning the decision trees for conversations one can optimize the dialogue management and minimize the number of turn-takes steps in the dialogue. The decision tree represents the chronological ordering of the actions via the parent-child relationship and uses an object frame to represent the information state. The findings can be successfully applied in dialogue applications, such as contact center solutions.

Olguin and Cortes (2006) presented methodology promises a simple way to identify dialogue act types for the construction of dialogue managers. The methodology proposed CART-style decision trees on a corpus data where predictor data are utterance duration and sentence mood and the target data is the dialogue act type; first, sentence mood is predicted from INTSINT intonation taggings. The utility of predicting sentence mood was shown by comparing trees where tagged sentence mood, predicted sentence mood and no sentence mood at all were assessed. The resulting decision trees can be represented as if-then rule sets which can be programmed into a dialogue management system to identify the dialogue act type of an unknown utterance.

Komatani et al. (2005) proposed an abstract structure of a database search task and model it in two modes: specifying query conditions and requesting detaile information. Then, define a set of very simple dialogue acts corresponding to the above dialogue model. Furthermore, they create a model to maintain query conditions as a tree structure, which can be used as a weight between attributes of query conditions. The constraints derived from these models are integrated by using a decision tree learning, so that the system can determine a dialogue act of the utterance and whether each content word should be accepted or rejected, even when it contains Automatic Speech Recognition (ASR) errors.

In classification-and-ranking, the decisions to choose from one response utterance over another require a considerable amount of domain knowledge. Hence, a knowledge-based approach as in deep generation is absolutely necessary. Deep generation determines the content of an utterance, or what to say, while the surface generation realizes the structure of the utterance, or determines how to say. Because deep generation requires a high degree of linguistic abstraction to produce fine-grained input specifications in order to drive the surface generators (Varges and Purver, 2006; Langkilde-Geary, 2002; Belz, 2007), its primary drawback is the classic problem of knowledge engineering bottleneck.

Overgeneration and ranking approaches to natural language generation have become increasingly popular (Paiva and Evans, 2005; Oh and Rudnicky, 2002). Overgeneration-and-ranking in dialogue processing performs mild overgeneration of candidate, followed by ranking to select the highest-ranked candidate as output (Varges and Purver, 2006). The main problem with this approach that it has to generate more candidates to form sentences. In addition to that, language models like n-gram have a built-in bias towards shorter strings is calculated as the likelihood of a string of words is the joint probability of the words. More precisely, the product of the probabilities of each word is given by n-1 preceding words (Belz, 2007). It is clear that this is not necessary for generation of dialogue utterances because all candidates must be treated equally, regardless of the length and the language rules.

In this study, we presented a response classification experiment based on user intentions using decision tree. The intention-based response generation systems require the task of classifying the response utterances into response classes. A response class contains all response utterances that are coherent to a particular input utterance. Classification-based NLG has been carried out for tasks in deep generation to guide the process of surface generation (Marciniak and Strube, 2004). However, as Stent (2002), former classification-based experiments do not take a full stochastic approach to response generation, but rather only in deep generation.

DIALOGUE CORPUS

The dialogue corpus SCHISMA (Schouwburg Informatie Systeem) is a collection of 64 text-based dialogues of a theater information and reservation system of tickets with the main objective of enabling users to make inquiries about theater performances scheduled and book a show of a wide range of options available. The corpus obtained through a series of Wizard of Oz experiments, built purposely for the acquisition of dialogue corpus for theater domain. The corpus contains 920 user utterances and 1127 server utterances in total. Schouwburg Infomatie System (SCHISMA) corpus is a mixed-initiative (Hulstijn and Van Hessen, 1998).

There are two types of interaction: inquiry and transaction. During inquiry the user has the initiative; the system answers the user’s questions. When the user has indicated that he or she wants a reservation transaction the system takes initiative. The system will ask user series of questions like number of tickets to reserve, discount cards and others. User will answer the questions to complete the reservation details required by the system.

In transaction dialogue, before it reaches the stage of booking, the user and the system must cooperate to reach agreement on several issues such as the value of the ticket, the seating arrangement or the availability of discount. This model is more complex than the answer to the question systems because the system at any time, either party may request information from each other, especially for the user, it might come back out of any previous decisions and to start talking about a total opposite direction (Traum, 1997).

SCHISMA corpus is tagged using dialogue act annotation scheme based on Dialogue Act Markup in Several Layers (DAMSL) framework by Keizer and Op den Akker (2007). Table 1 lists the dialogue acts, represented as FLFs and BLFs in SCHISMA corpus. SCHISMA-DAMSL consists of five layers, each of which covers different aspect of communicative functions. This study concerned on two levels only, the forward-looking and backward-looking functions. Both levels indicate the communicative functions of an utterance. FLF tags indicate the type of speech act that the utterance is conveying, for example, assert, info-request and commit. BLF tags indicate how the particular utterance relates to the previous utterance and include answers (positive, negative or no-feedback) to questions, degree of understanding or disagreement.

Table 1: FLF and BLF for SCHISMA
Image for - Improving Accuracy of Intention-Based Response Classification using Decision Tree

Image for - Improving Accuracy of Intention-Based Response Classification using Decision Tree
Fig. 1: The two-staged classification-and-ranking architecture

DECISION TREE FOR RESPONSE CLASSIFICATION

Decision tree learning is a method for approximating discrete-valued target functions, in which the learned function is represented by a decision tree. Learned trees can also be re-represented as sets of if-then rules to improve human readability. These learning methods are among the most popular algorithms and have been successfully applied to a broad range of tasks from learning to diagnose medical cases to learning to assess credit risk of loan applicants (Bar-Or et al., 2005). Decision trees classify instances by sorting them down the tree from the root to some leaf node, which provides the classification of the instance. Each node in the tree specifies a test of some attribute of the instance and each branch descending from that node corresponds to one of the possible values for this attribute (Mitchell, 1997).

Response classification is part of a two-staged classification-and-ranking architecture as shown in Fig. 1. This architecture proposed by Mustapha et al. (2008). The first component is a classifier that classifies user input utterances into response classes based on their contextual, pragmatic interpretations. The second component is a ranker that scores the candidate response utterances according to semantic content relevant to the input utterance.

One approach to classification would be to generate all possible decision trees that correctly classify the training set and to select the simplest of them. The number of such trees is finite but very large, so this approach would only be feasible for small classification tasks. ID3 decision tree was designed for the other end of the spectrum, where there are many attributes and the training set contains many objects, but where a reasonably good decision tree is required without much computation (Quinlan, 1986).

The basic structure of ID3 is iterative (Li and Aiken, 1998). A subset of the training set is chosen at random and a decision tree formed from it; this tree correctly classifies all instances in the subset. All other instances in the training set are then classified using the tree. If the tree gives the correct answer for all of these instances, it is correct for the entire training set and the process terminates. If not, a selection of the incorrectly classified instances is added to the subset and the process continues. This procedure will always produce a decision tree that correctly classifies each instance in the training set, provided that a test can always be found that gives a nontrivial partition of any set of instances. For ID3, the choice of test is the selection of an attribute for the root of the tree. ID3 adopts a mutual-information criterion to choose that attribute to branch on that gains the most information. So, the inductive biases inherent in ID3 are preference biases that explicitly search for a simple hypothesis.

The main task of any classification task is to identify the set of classes that some observation belongs to, which is in this paper to identify a response class for each response utterances, such that P(response class|user utterance). The purpose of the response classification is to find the proper recognition for the accuracy of correct predictions of response class rc, given the user utterance U.

The user utterances are characterized by semantic and pragmatic features represented by nodes in the decision tree, at each node selecting the utterance properties that uniquely constitute the user utterance U that best classified. This process continues until the tree perfectly classified, or until all features have been used. We use rc to mean our estimate of the correct response class.

EXPERIMENTAL OPERATIONS

The experiments concerned on classification of user input utterances into response classes based on features extracted from user input utterances. The SCHISMA provides 920 instances of user utterance from 64 dialogues. The response class for each user utterance is manually tagged according to topic of the response utterances.

Tagging the response class adapts to patterns of input and response utterance per turn throughout the course of conversation to maintain the coherency in a sequence of two utterances. There were 15 response classes using the same naming conventions as topic in user utterance. Table 2 shows the statistics for the response classes.

The 10-fold cross validation is performed to split the data into ten approximately equal partitions, each being used in turn for testing while the remainder of data is used for training.

Table 3 shows the semantic and pragmatic feature used in the classification experiment. The speech acts FLF and grounding acts BLF from user utterances readily available from the DAMSL-annotated SCHISMA corpus.

We extend our experiment by testing another dialogue corpus in order to validate the firmness of the decision tree classifier. Corresponding to response classification experiment in SCHIMA corpus, we investigate the accuracy rate for response classification task with cross-domain experimentation using MONROE corpus as the secondary source of our validation experiment.

The MONROE corpus is a mixed-initiative interaction and collaborative problem-solving task in disaster scenario set in Monroe County, New York. Emergencies include car accidents, natural disasters such as flooding and snow storms, request for medical assistance, or civil disorders. Given a particular emergency task, the dialogue participants are expected to coordinate help for the task. The objects to coordinate for in this domain include people, roads, vehicles, crews and equipment (Stent, 2002).

Table 2: Statistics for response classes
Image for - Improving Accuracy of Intention-Based Response Classification using Decision Tree

Table 3: Features used as nodes in decision tree
Image for - Improving Accuracy of Intention-Based Response Classification using Decision Tree

Table 4: Response classification accuracy comparison
Image for - Improving Accuracy of Intention-Based Response Classification using Decision Tree

Table 5: Performance evaluation of the three classifiers
Image for - Improving Accuracy of Intention-Based Response Classification using Decision Tree

Same with response classification experiment in SCHIMA corpus, we investigate the accuracy rate for response classification task. The baseline accuracy for response classification in MONROE corpus is 64.8% achieved with Bayesian networks and 50.8% achieved with Maximum Likelihood. Table 4 shows the results of classification experiment.

DISCUSSION

Table 4 relates the results for response classification experiment from our approach using decision tree with the previous findings which are the Bayesian networks as well as Maximum Likelihood (Mustapha et al., 2008) using two sets of corpus, SCHISMA and MONROE. We achieved in accuracy result of maximum 81.95% better than the 73.9% obtained using Bayesian networks tested on the SCHISMA dialogue corpus and 71.3% achieved by using Maximum Likelihood estimation. Regarding to MONROE dialogue corpus, decision tree achieved 75.2% accuracy better than the two baseline approaches.

Table 5 shows the performance evaluations of the three classifiers, where the decision tree classifier performs better and achieves higher precision score compare to Bayesian network and Maximum Likelihood approach. The result shows empirical evidence that our approach using decision tree achieved the aim of the study on improving the dialogue act classification to classify a user utterance into response class. The decision tree correctly classified the user utterance with higher per cent recognition accuracy than both baseline approaches.

The result differs from previous study is due to the attribute selection measure where decision tree uses the information gain principles to take into account the discriminative power of each attribute over classes, in order to choose the best attribute one as the root then grow downward. This process ensures that the decision tree correctly classified the attributes. On the contrary, the previous work such as Bayesian networks, uses hill climbing search has no mechanism to alter the network structure to remove the arc at later stage, resulting in large number of conditional probabilities to be considered and often practically difficult to find significant features that optimize the classification performance in feature selection tasks. And another earlier study, Maximum Likelihood relies on rigid frequency counts, which often result in incorrect classification.

CONCLUSION

This study focused on classification of response utterances into response classes using decision tree. The experiment showed that the decision tree as classifier is well suited for classification task, where the classifier performances achieved the best accuracy of 81.95%. However, in order to improve the performance of decision tree classifier still further, it is clear that we need to search and analyze the 18.05% of user utterance that incorrectly classified.

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