An adaptive statistical languagemodel is described, which successfullyintegrates long distancelinguistic information with other knowledge sources. Most existing statistical language models exploit only the immediate history of a text. To extract information from further back in the document's history, we propose and use trigger pairs as the basic information bearing elements. This allows the model to adapt its expectations to the topic of discourse. Next, statistical evidence from multiple sources must be combined. Traditionally, linear interpolation and its variants have been used, but these are shown here to be seriously deficient. Instead, we apply the principle of Maximum Entropy (ME). Each information source gives rise to a set of constraints, to be imposed on the combined estimate. The intersection of these constraints is the set of probability functions which are consistent with all the information sources. The function with the highest entropy within that set is the ME solution...

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speec-act-like

Identifying whether an utterance is a statement, question, greeting, and so forth is integral to effective automatic understanding of natural dialog. Little is known, however, about how such dialog acts (DAs) can be automatically classified in truly natural conversation. This study asks whether current approaches, which use mainly word information, could be improved by adding prosodic information. The study is based on more than 1000 conversations from the Switchboard corpus. DAs were handannotated, and prosodic features (duration, pause, F0, energy, and speaking rate) were automatically extracted for each DA. In training, decision trees based on these features were inferred

We describe a new approach for statistical modeling and detection of discourse structure for natural conversational speech. Our model is based on 42 'Dialog Acts' (DAs), (question, answer, backchannel, agreement, disagreement, apology, etc). We labeled 1155 conversations from the Switchboard (SWBD) database (Godfrey et al. 1992) of human-to-human telephone conversations with these 42 types and trained a Dialog Act detector based on three distinct knowledge sources: sequence of words which characterize a dialog act, prosodic features which characterize a dialog act, and a statistical Discourse Grammar. Our combined detector, although still in preliminary stages, already achieves a 65% Dialog Act detection rate based on acoustic waveforms, and 72% accuracy based on word transcripts. Using this detector to switch among the 42 Dialog-Act-Specific trigram LMs also gave us an encouraging but not statistically significant reduction in SWBD word error.

We describe a new approach for statistical modeling and detection of discourse structure for natural conversational speech. Our model is based on 42 `Dialog Acts' (DAs), (question, answer, backchannel, agreement, disagreement, apology, etc). We labeled 1155 conversations from the Switchboard (SWBD) database (Godfrey et al. 1992) of human-to-human telephone conversations with these 42 types and trained a Dialog Act detector based on three distinct knowledge sources: sequences of words which characterize a dialog act, prosodic features which characterize a dialog act, and a statistical Discourse Grammar. Our combined detector, although still in preliminary stages, already achieves a 65% Dialog Act detection rate based on acoustic waveforms, and 72% accuracy based on word transcripts. Using this detector to switch among the 42 dialog-act-specific trigram LMs also gave us an encouraging but not statistically significant reduction in SWBD word error. 1 Introduction The ability to model and...

We describe an integrated approach for statistical modeling of discourse structure for natural conversational speech. Our model is based on 42 `dialog acts' (e.g., Statement, Question, Backchannel, Agreement, Disagreement, Apology), which were hand-labeled in 1155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We developed several models and algorithms to automatically detect dialog acts from transcribed or automatically recognized words and from prosodic properties of the speech signal, and by using a statistical discourse grammar. All of these components were probabilistic in nature and estimated from data, employing a variety of techniques (hidden Markov models, N-gram language models, maximum entropy estimation, decision tree classifiers, and neural networks). In preliminary studies, we achieved a dialog act labeling accuracy of 65% based on recognized words and prosody, and an accuracy of 72% based on word transcripts. Since humans achiev...

Previous attempts to automatically determine multi-words as the basic unit for language modeling have been successful for extending bigram models [10, 9, 2, 8] to improve the perplexity ofthelanguage model and/or the word accuracy of the speech decoder. However, none ofthese techniques gave improvements over the trigram model so far, except for the rather controlled ATIS task [8]. We therefore propose an algorithm, that minimizes the perplexity improvement ofa bigram model directly. The new algorithm is able to reduce the trigram perplexity andalso achieves word accuracy improvements in the Verbmobil task. It is the natural counterpart of successful word classi cation algorithms for language modeling [4, 7] that minimize the leaving-one-out bigram perplexity. Wealso give some details on the usage of class nding techniques and m-gram models, which can be crucial to successful applications of this technique. 1.

this document. The first section, in chapter 3, develops a model for syntactic dependencies based on word-category n-grams. The second section, in chapter 4, extends this model by allowing short-range word relations to be captured through the incorporation of selected word n-grams.

In our effort to build spoken language translation systems we have extended our JANUS system to process spontaneous human-human dialogs in a new domain, two people trying to schedule a meeting. Trained on an initial database JANUS-2 is able to translate English and German spoken input in either English, German, Spanish, Japanese or Korean output. To tackle the difficulty of spontaneous human-human dialogs we improved the JANUS-2 recognizer along its three knowledgesourcesacousticmodels, dictionary andlanguage models. We developed a robust translation system which performs semantic rather than syntactic analysis and thus is particulary suited to processing spontaneous speech. We describe repair methods to recover from recognition errors. tes on spontaneo...

Introduction These days there's a computational version of everything. Computational biology, computational musicology, computational archaeology, and so on, ad infinitum. Even movies are going digital. This chapter, as you might have guessed by now, thus explores the computational side of pragmatics. Computational pragmatics might be defined as the computational study of the relation between utterances and context. Like other kinds of pragmatics, this means that computational pragmatics is concerned with indexicality, with the relation between utterances and action, with the relation between utterances and discourse, and with the relationship between utterances and the place, time, and environmental context of their being uttered. As Bunt and Black (2000) point out, computational pragmatics, like pragmatics in general, is especially concerned with INFERENCE. Four core inferential problems in pragmatics have received the most attention in the computational com