Extracting decisions from multi-party dialogue using directed graphical models and semantic similarity (original) (raw)

Modelling and detecting decisions in multi-party dialogue

Proceedings of the 9th …, 2008

We describe a process for automatically detecting decision-making sub-dialogues in transcripts of multi-party, human-human meetings. Extending our previous work on action item identification, we propose a structured approach that takes into account the different roles utterances play in the decisionmaking process. We show that this structured approach outperforms the accuracy achieved by existing decision detection systems based on flat annotations, while enabling the extraction of more fine-grained information that can be used for summarization and reporting.

Identifying relevant phrases to summarize decisions in spoken meetings

2008

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.

Detecting and Summarizing Action Items in Multi-Party Dialogue

This paper addresses the problem of identifying action items discussed in open-domain conversational speech, and does so in two stages: firstly, detecting the subdialogues in which action items are proposed, discussed and committed to; and secondly, extracting the phrases that accurately capture or summarize the tasks they involve. While the detection problem is hard, we show that we can improve accuracy by taking account of dialogue structure. We then describe a semantic parser that identifies potential summarizing phrases, and show that for some task properties these can be more informative than plain utterance transcriptions. * This work was supported by the CALO project (DARPA grant NBCH-D-03-0010). We also thank Gokhan Tür, Andreas Stolcke and Liz Shriberg for provision of ASR output and dialogue act tags for the ICSI corpus.

Towards a Representation for Understanding the Structure of Multiparty Conversations

2011

Dialog is a crucially important mode of communication. Understanding its structure is vital to a great number of technological innovations. Within the domain of meeting dialog applications alone, there is a need for navigating, summarizing, and extracting action items in recorded meetings and automated facilitation of meetings. These efforts, however, are hampered by a lack of agreement on how best to annotate meeting dialog to serve downstream applications. In this technical report, I describe of some of the efforts at representation and annotation of dialog acts, surveying some of the vast differences between approaches. In addition, in order to more directly compare different representations of discourse structure, two small, pilot annotations were carried out on a subset of the ICSI Meeting Corpus using different annotation schemes. The results support the idea that skew between different annotation systems renders them to some degree incompatible. Meetings are an extremely impo...

Toward Joint Segmentation and Classification of Dialog Acts in Multiparty Meetings

Lecture Notes in Computer Science, 2006

This paper investigates a scheme for joint segmentation and classification of dialog acts (DAs) of the ICSI Meeting Corpus based on hidden-event language models and a maximum entropy classifier for the modeling of word boundary types. Specifically, the modeling of the boundary types takes into account dependencies between the duration of a pause and its surrounding words. Results for the proposed method compare favorably with our previous work on the same task.

Real-time decision detection in multi-party dialogue

2009

We describe a process for automatically detecting decision-making sub-dialogues in multi-party, human-human meetings in real-time. Our basic approach to decision detection involves distinguishing between different utterance types based on the roles that they play in the formulation of a decision. In this paper, we describe how this approach can be implemented in real-time, and show that the resulting system's performance compares well with other detectors, including an off-line version.

Joint Segmentation and Classification of Dialog Acts in Multiparty Meetings

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006

A* based joint segmentation and classification of dialog acts in multiparty meetings

IEEE Workshop on Automatic Speech Recognition and Understanding, 2005., 2005

We investigate the use of the A* algorithm for joint segmentation and classification of dialog acts (DAs) of the ICSI Meeting Corpus. For the heuristic search a probabilistic framework is used that is based on DA-specific N-gram language models. Furthermore, two new metrics for performance evaluation are motivated and described and the influence of different metrics for performance evaluation is demonstrated. The proposed method is evaluated on both traditional and new metrics, and compared with our previous work on the same task.

Representing Dialog Progression for Dynamic State Assessment

While developing several spoken language dialog systems for information retrieval tasks, we found that representing the dialog progression along an axis was useful to facilitate dynamic evaluation of the dialog state. Since dialogs evolve from the first exchange until the end, it is of interest to assess whether an ongoing dialog is running smoothly or if is encountering problems. We consider that the dialog progression can be represented on two axes: a Progression axis, which represents the "good" progression of the dialog and an Accidental axis, which represents the accidents that occur, corresponding to misunderstandings between the system and the user. The time (in number of turns) used by the system to repair the accident is represented by the Residual Error, which is incremented when an accident occurs and is decremented when the dialog progresses. This error reflects the difference between a perfect (i.e., theoretical) dialog (e.g. without errors, miscommunication...) and the real ongoing dialog. One particularly interesting use of the dialog axis progression annotation is to extract problematic dialogs from large data collections for analysis, with the aim of improving the dia-This work was partially financed by the European Commission under the IST-2000-25033 AMITIES project. log system (Wright-Hastie, Prasad and Walker, 2002). In the context of the IST Amities project (Amities, 2001(Amities, -2004 we have extended this representation to the annotation of human-human dialogs recorded at a stock exchange call center and intend to use the extended representation in an automated natural dialog system for call routing.

Analysis and classification of cooperative and competitive dialogs

Interspeech 2015, 2015

Cooperative and competitive game dialogs are comparatively examined with respect to temporal, basic text-based, and dialog act characteristics. The condition-specific speaker strategies are amongst others well reflected in distinct dialog act probability distributions, which are discussed in the context of the Gricean Cooperative Principle and of Relevance Theory. Based on the extracted features, we trained Bayes classifiers and support vector machines to predict the dialog condition, that yielded accuracies from 90 to 100%. Taken together the simplicity of the condition classification task and its probabilistic expressiveness for dialog acts suggests a two-stage classification of condition and dialog acts.

Extracting decisions from multi-party dialogue using directed graphical models and semantic similarity (original) (raw)

Related papers