The CALO Meeting Assistant (MA) provides for distributed meeting capture, annotation, automatic transcription and semantic analysis of multiparty meetings, and is part of the larger CALO personal assistant system. This paper presents the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, topic identification and segmentation, question-answer pair identification, action item recognition, decision extraction, and summarization.

Repair or error-recovery strategies are an important design issue in spoken dialogue systems (SDSs) - how to conduct the dialogue when there is no progress (e.g. due to repeated ASR errors). Nearly all current SDSs use hand-crafted repair rules, but a more robust approach is to use reinforcement learning (RL) for data-driven dialogue strategy learning. However, as well as usually being tested only in simulation, current RL approaches use small state spaces which do not contain linguistically motivated features such as "dialogue acts" (DAs). We show that a strategy learned with DA features outperforms hand-crafted and slot-status strategies when tested with real users (+9% average task completion, p < 0.05). We then explore how using DAs produces better repair strategies e.g. focus-switching. We show that DAs are useful in de.ciding both when to use a repair strategy, and which one to use.

We address the problem of identifying words and phrases that accurately capture, or contribute to, the semantic gist of decisions made in multi-party human-human meetings. We first describe our approach to modelling decision discussions in spoken meetings and then compare two approaches to extracting information from these discussions. The first one uses an opendomain semantic parser that identifies candidate phrases for decision summaries and then employs machine learning techniques to select from those candidate phrases. The second one uses categorical and sequential classifiers that exploit simple syntactic and semantic features to identify words and phrases relevant for decision summarization.

The CALO Meeting Assistant provides for distributed meeting capture, annotation, automatic transcription and semantic analysis of multiparty meetings, and is part of the larger CALO personal assistant system. This paper summarizes the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, question-answer pair identification, action item recognition, decision extraction, and summarization.

We explore the use of restricted dialogue contexts in reinforcement learning (RL) of effective dialogue strategies for information seeking spoken dialogue systems (e.g. COMMUNICATOR (Walker et al., 2001)). The contexts we use are richer than previous research in this area, e.g. (Levin and Pieraccini, 1997; Scheffler and Young, 2001; Singh et al., 2002; Pietquin, 2004), which use only slot-based information, but are much less complex than the full dialogue "Information States" explored in (Henderson et al., 2005), for which tractabe learning is an issue. We explore how incrementally adding richer features allows learning of more effective dialogue strategies. We use 2 user simulations learned from COMMUNICATOR data (Walker et al., 2001; Georgila et al., 2005b) to explore the effects of different features on learned dialogue strategies. Our results show that adding the dialogue moves of the last system and user turns increases the average reward of the automatically learned strategies by 65.9% over the original (hand-coded) COMMUNICATOR systems, and by 7.8% over a baseline RL policy that uses only slot-status features. We show that the learned strategies exhibit an emergent "focus switching" strategy and effective use of the 'give help' action.