We address a spoken dialogue system which conducts information navigation in a style of small talk. The system uses Web news articles as an information source, and the user can receive information about the news of the day through interaction. The goal and procedure of this kind of dialogue are not well defined. An empirical approach based on a partially observable Markov decision process (POMDP) has recently been widely used for dialogue management, but it assumes a definite task goal and information slots, which does not hold in our application system. In this work, we formulate the problem of dialogue management as a selection of modules and optimize it with POMDP by tracking the dialogue state and focus of attention. The POMDP-based dialogue manager receives a user intention that is classified by a spoken language understanding (SLU) component based on logistic regression (LR). The manager also receives a user focus that is detected by the SLU component based on conditional random fields (CRFs). These dialogue states are used for selecting appropriate modules by policy function, which is optimized by reinforcement learning. The reward function is defined by the quality of interaction to encourage long interaction of information navigation with users. The module which responds to user queries is based on a similarity of predicate-argument (P-A) structures that are automatically defined from a domain corpus. It allows for flexible response generation even if the system cannot find exact matching information to the user query. The system also proactively presents information by following the user focus and retrieving a news article based on the similarity measure even if the user does not make any utterance. Experimental evaluations with real dialogue sessions demonstrate that the proposed system outperformed the conventional rule-based system in terms of dialogue state tracking and action selection. Effect of focus detection in the POMDP framework is also confirmed. (C) 2015 Elsevier Ltd. All rights reserved.