NLP@Pitt

Welcome!

The laboratory is co-directed by Diane Litman, Janyce Wiebe, and Rebecca Hwa. We are pursuing research in a wide range of natural language processing problems, including discourse and dialogue, spoken language processing, affective computing, natural language learning, statistical parsing, and machine translation.

The Intelligent Systems Program, a PhD graduate program in Artificial Intelligence at the University of Pittsburgh, is now accepting applications! For more informations, please see our admission requirements page. To apply, please fill out the online webform

[PRACTICE TALK] Swapna's Sigdial paper practice talk

Detecting Arguing and Sentiment in Meetings

This paper analyzes opinion categories like Sentiment and Arguing in meetings.
We first annotate the categories manually. We then develop genre-specific lexicons using interesting function word combinations for detecting the opinions. We analyze relations between dialog structure information and opinion expression in context of multi-party discourse. Finally we show that classifiers using lexical and discourse knowledge have significant improvement over baseline.

[PRACTICE TALK] Hua's Interspeech paper

Details soon.

[Practice Talk] Convergence and Learning

In this paper we examine whether the student-to-tutor convergence of lexical and speech features is a useful predictor of learning in a corpus of spoken tutorial dialogs. This possibility is raised by the Interactive Alignment Theory, which suggests a connection between convergence of speech features and the amount of semantic alignment between partners in a dialog. A number of studies have shown that users converge their speech productions toward dialog systems. If, as we hypothesize, semantic alignment between a student and a tutor (or tutoring system) is associated with learning, then this convergence may be correlated with learning gains. We present evidence that both lexical convergence and convergence of an acoustic/prosodic feature are useful features for predicting learning in our corpora. We also find that our measure of lexical convergence provides a stronger correlation with learning in a human/computer corpus than did a previous measure of lexical cohesion.

[PRACTICE TALK] Josh, Rebecca on MT Evaluations

Abstracts etc. TBA

[PRACTICE TALK] Mihai's ACL paper practice talk

Paper title: The Utility of a Graphical Representation of Discourse Structure in Spoken Dialogue Systems

Authors:
Mihai Rotaru and Diane J. Litman

Abstract:
In this paper we explore the utility of the Navigation Map (NM), a graphical representation of the discourse structure. We run a user study to investigate if users perceive the NM as helpful in a tutoring spoken dialogue system. From the users’ perspective, our results show that the NM presence allows them to better identify and follow the tutoring plan and to better integrate the instruction. It was also easier for users to concentrate and to learn from the system if the NM was present. Our preliminary analysis on objective metrics further strengthens these findings.

[TALK] Adam Lopez (UMD)

Hierarchical Phrase-Based Translation with Suffix Arrays

A major engineering challenge in statistical machine translation systems is the efficient representation of extremely large translation rulesets. In phrase-based models, this problem can be addressed by storing the training data in memory and using a suffix array as an efficient index to quickly lookup and extract rules on the fly. Hierarchical phrase-based translation introduces the added wrinkle of source phrases with gaps. Lookup algorithms used for contiguous phrases no longer apply and the best approximate pattern matching algorithms are much too slow, taking several minutes per sentence. I describe new lookup algorithms for hierarchical phrase- based translation that reduce the empirical computation time by nearly two orders of magnitude, making on-the-fly lookup feasible for source phrases with gaps. I will also discuss some novel applications of these algorithms.

Speaker Bio

Adam Lopez is a Ph.D. candidate in computer science at the University of Maryland, expecting to graduate in August 2007. His dissertation work focuses on statistical machine translation and his interests are in large-scale natural language processing and algorithms. Prior to graduate school, he worked as a software engineer at the IBM Corporation, after receiving his bachelor's degree in computer science from Duke University.

[TALK] Dialogue Research in Toyota Central Labs

Presenter: Ryoko TOKUHISA ( Toyota Central R&D Labs )

NOTE - THIS TALK WILL BE AT 12 NOON!

I introduce the overview of the researches in Toyota Central Labs. We are developing the dialogue system for the car navigation system and the home robot. I mainly work on the affective dialogue of the home robot, so that it would be closely connected with the Emotion Detection in Tutoring task and Opinion type analysis.

[NEWS] Best Paper Award

Congratulations to Kate Forbes-Riley, Mihai Rotaru, Diane Litman, and Joel Tetrault, for getting a Best Paper Award (Late-Breaking News category) at NAACL-HLT 2007 for "Exploring Affect-Context Dependencies for Adaptive System Development"

Ph.D. proposal defense - Mihai Rotaru

CANDIDATE: Mihai Rotaru
TITLE: Applications of Discourse Structure for Spoken Dialogue Systems
WHEN: Tuesday, March 20, 1 pm
WHERE: 5317 Sennott Hall (5th floor conference room)

COMMITTEE MEMBERS:
Diane J. Litman (advisor)
Rebecca Hwa
Carolyn P. Rosé
Janyce M. Wiebe

ABSTRACT:
Just as words in a utterance are organized in a structure (e.g. syntactic, semantic), utterances in a discourse (monologue or dialogue) are organized in structure called the discourse structure. Our proposed work investigates the utility of discourse structure for spoken dialogue systems (computer systems that interact with users via speech).

Two types of applications are being pursued: on the system side and on the user side. On the system side, we investigate if the discourse structure information is useful for various spoken dialogue system tasks: performance analysis, characterization of user affect and characterization of speech recognition problems. On the user side, we investigate whether the discourse structure information is useful for users through a graphical representation of the discourse structure.

QA with Attitude: Exploiting Opinion Type Analysis for Improving Question Answering

Speaker: Swapna Somasundaran

Room : Board room ( 6th floor - room 6329) Sennot Square

Time : 9:00 am

Practice talk for ICWSM-07.

Abstract
In this work, we explore the utility of attitude types for improving question answering (QA) on both web-based discussions and news data. We present a set of attitude types developed with an eye toward QA and show that they can be reliably annotated. Using the attitude annotations, we develop automatic classifiers for recognizing two main types of attitudes: sentiment and arguing. Finally, we exploit information about the attitude types of questions and answers for improving opinion QA with promising results.