Prompt and knowledgeable responses to customers’ email are critical in maximizing customer satisfaction. Such messages often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these email messages as emotional email. They provide valuable feedback to improve contact center efficiency and the quality of the overall customer care experience, which in turn results in increased customer retention. We describe a method that uses salient features to identify emotional email in the customer care domain. Salient features in customer care related email are expressions of customer frustration, dissatisfaction with the business, and threats to either leave, take legal action, and/or report to authorities. Compared to a baseline system using word unigram features, our proposed approach significantly improves emotional email detection performance.

Spoken language understanding (SLU) aims at extracting meaning from natural language speech. Over the past decade, a variety of practical goal-oriented spoken dialog systems have been built for limited domains. SLU in these systems ranges from understanding predetermined phrases through fixed grammars, extracting some predefined named entities, extracting users' intents for call classification, to combinations of users' intents and named entities. In this paper, we present the SLU system of VoiceTone/spl reg/ (a service provided by AT&T where AT&T develops, deploys and hosts spoken dialog applications for enterprise customers). The SLU system includes extracting both intents and the named entities from the users' utterances. For intent determination, we use statistical classifiers trained from labeled data, and for named entity extraction we use rule-based fixed grammars. The focus of our work is to exploit data and to use machine learning techniques to create scalable SLU systems which can be quickly deployed for new domains with minimal human intervention. These objectives are achieved by 1) using the predicate-argument representation of semantic content of an utterance; 2) extending statistical classifiers to seamlessly integrate hand crafted classification rules with the rules learned from data; and 3) developing an active learning framework to minimize the human labeling effort for quickly building the classifier models and adapting them to changes. We present an evaluation of this system using two deployed applications of VoiceTone/spl reg/.

The use of boosting for call classification in spoken language understanding is described in this paper. An extension to the AdaBoost algorithm is presented that permits the incorporation of prior knowledge of the application as a means of compensating for the large dependence on training data. We give a convergence result for the algorithm, and we describe experiments on four datasets showing that prior knowledge can substantially improve classification performance.

Spontaneous human utterances in the context of human-human and human-machine dialogs are rampant with dysfluencies, and speech repairs. Furthermore, when recognized using a speech recognizer, these utterances produce a sequence of words with no identification of clausal units. Such long strings of words combined with speech errors pose a difficult problem for spoken language parsing and understanding. In this paper, we address the issue of editing speech repairs as well as segmenting user utterances into clause units with a view of parsing and understanding spoken language utterances. We present generative and discriminative models for this task and present evaluation results on the human-human conversations obtained from the Switch board corpus.

Data collection and annotation are major bottlenecks in rapid development of accurate syntactic and semantic models for natural-language dialogue systems. In this paper we show how human knowledge can be used when designing a language understanding system in a manner that would alleviate the dependence on large sets of data. In particular, we extend BoosTexter, a member of the boosting family of algorithms, to combine and balance hand-crafted rules with the statistics of available data. Experiments on two voice-enabled applications for customer care and help desk are presented.

MATRIXx/PCTMallows engineers to rapidly build system models interactively and graphically on personal computers and to simulate and optimize system characteristics based on these models. The complete model can be developed by modeling each aspect or component of a system and the manner in which these components interact. Performance may be evaluated under nominal and off-design conditions. Model-building, simulation, and optimization are thus available in an integrated, interactive, graphical, and user- friendly package. The software requires no knowledge of standard programming languages. It has been used by engineers, technical managers, and technical specialists. Interfaces to other productivity tools will integrate graphics output with text, thus documenting analyses results for presentations, reports, and communication to other divisions in a company. A unique networking capability allows a number of personal computers to share databases or to transfer large computationally intensive problems to a mainframe.

An adaptive threshold element is able to "learn" a strategy of play for the game blackjack (twenty-one) with a performance close to that of the Thorp optimal strategy although the adaptive system has no prior knowledge of the game and of the objective of play. After each winning game the decisions of the adaptive system are "rewarded." After each losing game the decisions are "punished." Reward is accomplished by adapting while accepting the actual decision as the desired response. Punishment is accomplished by adapting while taking the desired response to be the opposite of that of the actual decision. This learning scheme is unlike "learning with a teacher" and unlike "unsupervised learning." It involves "bootstrap adaptation" or "learning with a critic." The critic rewards decisions which are members of successful chains of decisions and punishes other decisions. A general analytical model for learning with a critic is formulated and analyzed. The model represents bootstrap learning per se. Although the hypotheses on which the model is based do not perfectly fit blackjack learning, it is applied heuristically to predict adaptation rates with good experimental success. New applications are being explored for bootstrap learning in adaptive controls and multilayered adaptive systems.