Verena Rieser

Implications for Generating Clarification Requests in Task-Oriented Dialogues

Clarification requests (CRs) in conversation ensure and maintain mutual understanding and thus play a crucial role in robust dialogue interaction. In this paper, we describe a corpus study of CRs in task-oriented dialogue and compare our findings to those reported in two prior studies. We find that CR behavior in task-oriented dialogue differs significantly from that in everyday conversation in a number of ways. Moreover, the dialogue type, the modality and the channel quality all influence the decision of when to clarify and at which level of the grounding process. Finally we identify form-function correlations which can inform the generation of CRs.

Using Machine Learning to Explore Human Multimodal Clarification Strategies

We investigate the use of machine learning in combination with feature engineering techniques to explore human multimodal clarification strategies and the use of those strategies for dialogue systems. We learn from data collected in a Wizard-of-Oz study where different wizards could decide whether to ask a clarification request in a multimodal manner or else use speech alone. We show that there is a uniform strategy across wizards which is based on multiple features in the context. These are generic runtime features which can be implemented in dialogue systems. Our prediction models achieve a weighted f-score of 85.3% (which is a 25.5% improvement over a one-rule baseline). To assess the effects of models, feature discretisation, and selection, we also conduct a regression analysis. We then interpret and discuss the use of the learnt strategy for dialogue systems. Throughout the investigation we discuss the issues arising from using small initial Wizard-of-Oz data sets, and we show that feature engineering is an essential step when learning from such limited data.

USING LOGISTIC REGRESSION TO INITIALISE REINFORCEMENT-LEARNING-BASED DIALOGUE SYSTEMS

We investigate the use of logistic regression (LR) to initialise reinforcement learning (RL)-based dialogue systems with models of human dialogue strategies. LR produces accurate predictions and performs feature selection. We illustrate this technique in exploring human multimodal clarification strategies, observed in a Wizard-of-Oz experiment. We use it to initialise an RL-based system with features which significantly influence human behaviour. We show that the strategy applied by the human wizards is sensitive to different dialogue contexts. Furthermore we show that for predicting clarification behaviour the logistic models improve over the baseline on average twice as much as the supervised learning techniques used in previous work.

The SAMMIE multimodal dialogue corpus meets the nite XML toolkit

We demonstrate work in progress using the Nite XML Toolkit on a corpus of multimodal dialogues with an MP3 player collected in a Wizard-of-Oz (WOZ) experiments and annotated with a rich feature set at several layers. We designed an NXT data model, converted experiment log file data and manual transcriptions into NXT, and are building annotation tools using NXT libraries.