NLP@Pitt

Using Reinforcement Learning to Build a Better Model of Dialogue State - Joel Tetreault

(Practice talk for EACL one week later) Given the growing complexity of tasks that spoken dialogue systems are trying to handle, Reinforcement Learning (RL) has been increasingly used as a way of automatically learning the best policy for a system to make. While most work has focused on generating better policies for a dialogue manager, very little work has been done in using RL to construct a better dialogue state. This talk presents a RL approach for determining what dialogue features are important to a spoken dialogue tutoring system. Our experiments show that incorporating dialogue factors such as dialog acts, emotion, repeated concepts and performance play a significant role in tutoring and should be taken into account when designing dialogue systems.

[TALK] The role of discourse structure in analyzing spoken tutoring dialogues

Speaker: Mihai Rotaru

Based on recent advancements in spoken dialogue technologies, researchers have begun implementing spoken dialogue systems in more complex domains. This work is part of our ongoing project that studies the challenges posed by the tutoring domain to spoken dialogue design. Our approach is to study dependencies between speech recognition problems and various dialogue factors. In our previous work, we found interesting results using this methodology: chaining effects for certain speech recognition problems (our Interspeech 2005 paper) and interactions with certainty, correctness and frustration/anger (paper submitted to ACL 2006).

In this presentation, I talk about our preliminary results that analyze the role of dialogue structure for understanding several dialogue phenomena.

Building an English-Iraqi Arabic Machine Translation System for Spoken Utterances with Limited Resources

Presentation by: Behrang Mohit

I will talk about the challenge of Speech Translation and the ways that we used a text translation system to build a speech translator. Specifically, our efforts were aimed at leveraging from the resources in Modern Standard Arabic (MSA) to enrich the translation and language models of a speech translation system for the Iraqi Arabic and English.
This is a joint work with Jason Riesa, Kevin Knight and Daniel Marcu.

[News] Congratulations Art and Behrang

Congratulations to Art and Behrang, who were awarded Mellon Fellowships for the 2006-2007 academic year. Andrew Mellon Predoctoral Fellowships are awarded to students of exceptional ability and promise who are enrolled or wish to enroll at the University of Pittsburgh in programs leading to the Ph.D. in various fields of the humanities, the natural sciences and the social sciences.

March 1, 2006: Comparing Synthesized versus Pre-recorded Tutor Speech in an Intelligent

Note: We will meet at 1 PM!! (Talk by Kate Forbes-Riley, Wed., March 1) This is a practice talk for our paper accepted at the FLAIRS 2006 conference (with: Diane Litman and Scott Silliman and Joel Tetreault). Here's the abstract for the paper: We evaluate the impact of tutor voice quality in the context of our intelligent tutoring spoken dialogue system. We first describe two versions of our system which yielded two corpora of human-computer tutoring dialogues: one using a tutor voice pre-recorded by a human, and the other using a low-cost text-to-speech tutor voice. We then discuss the results of two-tailed t-tests comparing student learning gains, system usability, and dialogue efficiency across the two corpora. Overall, our results suggest that although the quality of the tutor voice does not significantly impact student learning gains, it does impact the usability and efficiency of our tutoring system.