Articles

This paper describes the CMU Let’s Go!! bus information system, an experimental system designed to study the use of spoken dialogue interfaces by non-native speakers. The differ- ences in performance of the speech recogni- tion and language understanding modules of the system when confronted with native and non-native spontaneous speech …...

End-to-end evaluations of conversational dia- logue systems with naive users are currently uncovering severe usability problems that re- sult in low task completion rates. Preliminary analyses suggest that these problems are related to the system’s dialogue management and turn- taking behavior. We present the results of ex- periments designed to …...

We examine the utility of multiple types of turn-level and contextual linguistic features for automatically predicting student emotions in human-human spoken tutoring dialogues. We first annotate student turns in our corpus for negative, neutral and positive emotions. We then automatically extract features represent- ing acoustic-prosodic and other linguistic in- formation …...

We demonstrate a new research approach to the problem of predicting the reading difficulty of a text passage, by recasting readability in terms of statistical language modeling. We derive a measure based on an extension of multinomial naïve Bayes classification that combines multiple language models to estimate the most likely …...

This paper describes the application of discrim- inative reranking techniques to the problem of machine translation. For each sentence in the source language, we obtain from a baseline sta- tistical machine translation system, a ranked � - best list of candidate translations in the target language. We introduce two novel …...

The paper presents two approaches to interactively refining user search formulations and their evaluation in the new High Accuracy Retrieval from Documents (HARD) track of TREC-12. One method consists of asking the user to select a number of sentences that may represent relevant documents, and then using the documents, whose …...

We present Minimum Bayes-Risk (MBR) de- coding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance. We de- scribe a hierarchy of loss functions that incor- porate different levels of linguistic information from word strings, word-to-word alignments …...

We present an empirically grounded method for evaluating content selection in summariza- tion. It incorporates the idea that no single best model summary for a collection of documents exists. Our method quantifies the relative im- portance of facts to be conveyed. We argue that it is reliable, predictive and diagnostic, …...

The regular occurrence of disfluencies is a distinguishing characteristic of spontaneous speech. Detecting and removing such disflu- encies can substantially improve the usefulness of spontaneous speech transcripts. This pa- per presents a system that detects various types of disfluencies and other structural information with cues obtained from lexical and prosodic …...

In this paper we propose a data intensive ap- proach for inferring sentence-internal tempo- ral relations, which relies on a simple prob- abilistic model and assumes no manual cod- ing. We explore various combinations of fea- tures, and evaluate performance against a gold- standard corpus and human subjects perform- ing …...

Recent work on spoken document retrieval has suggested that it is adequate to take the single- best output of ASR, and perform text retrieval on this output. This is reasonable enough for the task of retrieving broadcast news stories, where word error rates are relatively low, and the stories are …...

Previous work demonstrated that web counts can be used to approximate bigram frequen- cies, and thus should be useful for a wide va- riety of NLP tasks. So far, only two gener- ation tasks (candidate selection for machine translation and confusion-set disambiguation) have been tested using web-scale data sets. The …...

We describe a methodology for rapid exper- imentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring …...

Many probabilistic models for natural language are now written in terms of hierarchical tree structure. Tree-based modeling still lacks many of the standard tools taken for granted in (finite- state) string-based modeling. The theory of tree transducer automata provides a possible frame- work to draw on, as it has been …...

Supervised estimation methods are widely seen as being superior to semi and fully unsuper- vised methods. However, supervised methods crucially rely upon training sets that need to be manually annotated. This can be very ex- pensive, especially when skilled annotators are required. Active learning (AL) promises to help reduce this …...

This paper investigates the usefulness of sentence-internal prosodic cues in syntac- tic parsing of transcribed speech. Intu- itively, prosodic cues would seem to pro- vide much the same information in speech as punctuation does in text, so we tried to incorporate them into our parser in much the same way …...

This paper describes an automatic method for acquiring hyponymy relations from HTML documents on the WWW. Hyponymy relations can play a crucial role in various natural lan- guage processing systems. Most existing ac- quisition methods for hyponymy relations rely on particular linguistic patterns, such as “NP such as NP”. Our …...

The field of Psychometrics routinely grapples with the question of what it means to measure the inherent ability of an organism to perform a given task, and for the last forty years, the field has increasingly relied on probabilistic methods such as the Rasch model for test con- struction and …...

We consider the problem of modeling the con- tent structure of texts within a specific do- main, in terms of the topics the texts address and the order in which these topics appear. We first present an effective knowledge-lean method for learning content models from un- annotated documents, utilizing a …...

In this paper we describe and evaluate a Ques- tion Answering system that goes beyond an- swering factoid questions. We focus on FAQ- like questions and answers, and build our sys- tem around a noisy-channel architecture which exploits both a language model for answers and a transformation model for an- …...