Srinivasan Chandrasekaran Janarthanam

A Two-Tier User Simulation Model for Reinforcement Learning of Adaptive Referring Expression Generation Policies

We present a new two-tier user simulation model for learning adaptive referring expression generation (REG) policies for spoken dialogue systems using reinforcement learning. Current user simulation models that are used for dialogue policy learning do not simulate users with different levels of domain expertise and are not responsive to referring expressions used by the system. The two-tier model displays these features, that are crucial to learning an adaptive REG policy. We also show that the two-tier model simulates real user behaviour more closely than other baseline models, using the dialogue similarity measure based on Kullback-Leibler divergence.

Named entity transliteration for cross-language information retrieval using compressed word format mapping algorithm

Transliteration of named entities in user queries is a vital step in any Cross-Language Information Retrieval (CLIR) system. Several methods for transliteration have been proposed till date based on the nature of the languages considered. In this paper, we present a transliteration algorithm for mapping English named entities to their proper Tamil equivalents. Our algorithm employs a grapheme-based model, in which transliteration equivalents are identified by mapping the source language names to their equivalents in a target language database, instead of generating them. The basic principle is to compress the source word into its minimal form and align it across an indexed list of target language words to arrive at the top n-equivalents based on the edit distance. We compare the performance of our approach with a statistical generation approach using Microsoft Research India (MSRI) transliteration corpus. Our approach has proved very effective in terms of accuracy and time.

Towards dialogue dimensions for a robotic tutor in collaborative learning scenarios

There has been some studies in applying robots to education and recent research on socially intelligent robots show robots as partners that collaborate with people. On the other hand, serious games and interaction technologies have also proved to be important pedagogical tools, enhancing collaboration and interest in the learning process. This paper relates to the collaborative scenario in EMOTE EU FP7 project and its main goal is to develop and present the dialogue dimensions for a robotic tutor in a collaborative learning scenario grounded in human studies. Overall, seven dialogue dimensions between the teacher and students interaction were identified from data collected over 10 sessions of a collaborative serious game. Preliminary results regarding the teachers perspective of the students interaction suggest that student collaboration led to learning during the game. Besides, students seem to have learned a number of concepts as they played the game. We also present the protocol that was followed for the purposes of future data collection in human-human and human-robot interaction in similar scenarios.

How Expressiveness of a Robotic Tutor is Perceived by Children in a Learning Environment

We present a study investigating the expressiveness of two different types of robots in a tutoring task. The robots used were i) the EMYS robot, with facial expression capabilities, and ii) the NAO robot, without facial expressions but able to perform expressive gestures. Preliminary results show that the NAO robot was perceived to be more friendly, pleasant and empathic than the EMYS robot as a tutor in a learning environment.

A Wizard-of-Oz Environment to Study Referring Expression Generation in a Situated Spoken Dialogue Task

We present a Wizard-of-Oz environment for data collection on Referring Expression Generation (REG) in a real situated spoken dialogue task. The collected data will be used to build user simulation models for reinforcement learning of referring expression generation strategies.

Empathic Robotic Tutors: Map Guide

In this demonstration we describe a scenario developed in the EMOTE project. The overall goal of the project is to develop an empathic robot tutor for 11-13 year old school students in an educational setting. We are aiming to develop an empathic robot tutor to teach map reading skills with this scenario on a touch-screen device.

Towards a serious game playing empathic robotic tutorial dialogue system

There are several challenges in applying conversational social robots to Technology Enhanced Learning and Serious Gaming. In this paper, we focus in particular on the dialogue management issues in building an empathic robotic tutor that plays a multi-person serious game with students to help them learn and understand the underlying educational concepts. Categories and Subject Descriptors I.2.11 [Intelligent Agents]: [Dialogue management]

Affective Feedback for a Virtual Robot in a Real-World Treasure Hunt

We explore the effect of the behaviour of a virtual robot agent in the context of a real-world treasure-hunt activity carried out by children aged 11-12. We compare three conditions: a traditional paper-based treasure hunt, along with a virtual robot on a tablet which provides either neutral or affective feedback during the treasure hunt. The initial results of the study suggest that the use of the virtual robot increased the perceived difficulty of the instruction-following task, while the affective robot feedback in particular made the questions seem more difficult to answer.

Learning adaptive referring expression generation policies for spoken dialogue systems

Adaptive generation of referring expressions in dialogues is beneficial in terms of grounding between the dialogue partners. However, handcoding adaptive REG policies is hard. We present a reinforcement learning framework to automatically learn an adaptive referring expression generation policy for spoken dialogue systems.

Multi-threaded Interaction Management for Dynamic Spatial Applications

We present a multi-threaded Interaction Manager (IM) that is used to track different dimensions of user-system conversations that are required to interleave with each other in a coherent and timely manner. This is explained in the context of a spoken dialogue system for pedestrian navigation and city question-answering, with information push about nearby or visible points-of-interest (PoI).