Narendra K. Gupta

The AT&T spoken language understanding system

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/.

Combining prior knowledge and boosting for call classification in spoken language dialogue

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.

EMOTION DETECTION IN EMAIL CUSTOMER CARE

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.

Segmenting spoken language utterances into clauses for semantic classification

Robust spoken language understanding in large-scale conversational dialog applications is usually performed by classification of the user utterances into one or many semantic classes. The features used for classification are sensitive to variations caused by artifacts of spoken language, such as edits, repairs and other dysfluencies. Furthermore, the performance of these classifiers typically degrades when the users utterance contains multiple semantic classes. In this paper, we present a semantic classification technique that first automatically removes dysfluencies and segments the users utterance into clauses and then classifies the utterance based on the classification of the clauses. We show that this preprocessing improves the semantic classification accuracy for utterances and significantly decreases the amount of training data needed for a given classification accuracy level.

Extracting Clauses for Spoken Language Understanding in Conversational Systems

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.