NLP@Pitt

[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. Ros�
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.

[TALK] Ray Mooney December 8

Learning to Extract Proteins and their Interactions from Biomedical Text (2:00, Room 5313 Sensq) - NOTE UNUSUAL TIME AND ROOM Learning to Extract Proteins and their Interactions from Biomedical Text Raymond J. Mooney University of Texas at Austin Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data on human genes from the 11 million abstracts in Medline. We have developed and evaluated a variety of learned information-extraction systems for identifying human proteins and their interactions in Medline abstracts. We will present our current best results on identifying names of human proteins using Conditional Random Fields and Relational Markov Networks. We will also present our current best results on identifying interactions between proteins using a Support Vector Machine with an underlying string kernel. Finally, we will summarize results from a recent large-scale application of our techniques, in which we mined 753,459 Medline abstracts to extract a database of 6,580 interactions between 3,737 human proteins. By merging this extracted data with existing databases, we have constructed (to our knowledge) the largest database of known human-protein interactions containing 31,609 interactions amongst 7,748 proteins. Bio: Raymond J. Mooney is a Professor in the Department of Computer Sciences at the University of Texas at Austin. He received his Ph.D. in 1988 from the University of Illinois at Urbana/Champaign. He is an author of over 100 published research papers, primarily in the area of machine learning. He was program co-chair of the 2006 National Conference on Artificial Intelligence, general chair of the 2005 joint Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, co-chair of the 1990 International Conference on Machine Learning, a recipient of the Best Research Paper Award at the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, a former editor of the Machine Learning journal, and a Fellow of the American Association for Artificial Intelligence. His recent research has focused on learning for natural-language processing, text mining, statistical relational learning, transfer learning, active learning, semi-supervised learning, bioinformatics, and autonomic computing.

[news] Congratulations to Greg Nicholas

Greg received an Honorable Mention for CRA's Outstanding Undergraduate Award for 2007!

Measuring Lexical and Acoustic/Prosodic Priming in Dialogs

Speaker: Art Ward Experimental research has shown that human users will converge with dialog systems along many dimensions of speech, including those of acoustic/prosodic features and lexical choice. Other results suggest that speech convergence may provide a variety of benefits to spoken dialog systems, such as increased ease of use, improved feelings of intimacy, and increased compliance on the part of the user. These potential benefits to dialog systems of detecting or generating convergence behaviors suggest the need for corpus studies of convergence, in addition to the experimental results. Here, we build on previous work to demonstrate corpus measures of lexical and acoustic/prosodic priming. We show that these measures successfully distinguish randomized from naturally ordered data, and demonstrate both lexical and acoustic/prosodic priming effects in our corpus of human/human tutoring dialogs.

How much data is enough? (Experiments with Confidence Bounds for MDP's)

Speaker: Joel Tetreault Data sparsity is one of the major issues that NLP researchers always wrestle with. That is, does one have enough data to make reliable conclusions in an experiment? Using Reinforcement Learning to improve a spoken dialogue system is no exception. Past approaches in this area have simply assumed that there was enough collected data to support a certain state and action space, or used thousands of user simulations to overcome the sparsity issue. In this talk, we present a methodology of confidence bounds on the expected reward to address the problem of data sparsity in MDP's. We show how this methodology works by apply it to a prior experiment of using MDP's to predict the best features to include in a model of the dialogue state. We also show how this approach has applications in model switching and user simulations.

Comparing Real-Real, Simulated-Simulated, and Simulated-Real Spoken Dialogue Corpora

Speaker: Hua Ai

Purpose: Prelim Exam

Abstract: User simulation is used to generate large corpora for using reinforcement learning to automatically learn the best policy for spoken dialogue systems. Although this approach is becoming increasingly popular, the differences between simulated and real corpora are not well studied. We build two simulation models to interact with an intelligent tutoring system. Both models are trained on two different real corpora separately. We use several evaluation measures proposed in previous research to compare between our two simulated corpora, between the original two real corpora, and between the simulated and real corpora. We next examine the differentiating power of these measures. Our results show that although these simple statistical measures can distinguish real corpora from simulated ones, these measures cannot help us to draw a conclusion on the �reality� of the simulated corpora since even two real corpora can be very different when evaluated on the same measures.