Toward Connecting Speech Acts and Search Actions in Conversational Search Tasks

Publication Date

1-1-2023

Document Type

Conference Proceeding

Publication Title

Proceedings of the ACM/IEEE Joint Conference on Digital Libraries

Volume

2023-June

DOI

10.1109/JCDL57899.2023.00027

First Page

119

Last Page

131

Abstract

Conversational search systems can improve user experience in digital libraries by facilitating a natural and intuitive way to interact with library content. However, most conversational search systems are limited to performing simple tasks and controlling smart devices. Therefore, there is a need for systems that can accurately understand the user's information requirements and perform the appropriate search activity. Prior research on intelligent systems suggested that it is possible to comprehend the functional aspect of discourse (search intent) by identifying the speech acts in user dialogues. In this work, we automatically identify the speech acts associated with spoken utterances and use them to predict the system-level search actions. First, we conducted a Wizard-of-Oz study to collect data from 75 search sessions. We performed thematic analysis to curate a gold standard dataset - containing 1,834 utterances and 509 system actions - of human-system interactions in three information-seeking scenarios. Next, we developed attention-based deep neural networks to understand natural language and predict speech acts. Then, the speech acts were fed to the model to predict the corresponding system-level search actions. We also annotated a second dataset to validate our results. For the two datasets, the best-performing classification model achieved maximum accuracy of 90.2% and 72.7% for speech act classification and 58.8% and 61.1 %, respectively, for search act classification.

Keywords

Conversational Search Systems, Dialogue Acts, Experimental User Study, Speech Acts, Spoken Search, Wizard-of-Oz Study

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