169,311 research outputs found
Joint Modeling of Content and Discourse Relations in Dialogues
We present a joint modeling approach to identify salient discussion points in
spoken meetings as well as to label the discourse relations between speaker
turns. A variation of our model is also discussed when discourse relations are
treated as latent variables. Experimental results on two popular meeting
corpora show that our joint model can outperform state-of-the-art approaches
for both phrase-based content selection and discourse relation prediction
tasks. We also evaluate our model on predicting the consistency among team
members' understanding of their group decisions. Classifiers trained with
features constructed from our model achieve significant better predictive
performance than the state-of-the-art.Comment: Accepted by ACL 2017. 11 page
Built to Last or Built Too Fast? Evaluating Prediction Models for Build Times
Automated builds are integral to the Continuous Integration (CI) software
development practice. In CI, developers are encouraged to integrate early and
often. However, long build times can be an issue when integrations are
frequent. This research focuses on finding a balance between integrating often
and keeping developers productive. We propose and analyze models that can
predict the build time of a job. Such models can help developers to better
manage their time and tasks. Also, project managers can explore different
factors to determine the best setup for a build job that will keep the build
wait time to an acceptable level. Software organizations transitioning to CI
practices can use the predictive models to anticipate build times before CI is
implemented. The research community can modify our predictive models to further
understand the factors and relationships affecting build times.Comment: 4 paged version published in the Proceedings of the IEEE/ACM 14th
International Conference on Mining Software Repositories (MSR) Pages 487-490.
MSR 201
An architecture for organisational decision support
The Decision Support (DS) topic of the Network Enabled Capability for Through Life Systems Engineering (NECTISE) project aims to provide organisational through-life decision support for the products and services that BAE Systems deliver. The topic consists of five streams that cover resource capability management, decision management, collaboration, change prediction and integration. A proposed architecture is presented for an Integrated Decision Support Environment (IDSE) that combines the streams to provide a structured approach to addressing a number of issues that have been identified by BAE Systems business units as being relevant to DS: uncertainty and risk, shared situational awareness, types of decision making, decision tempo, triggering of decisions, and support for autonomous decision making. The proposed architecture will identify how either individuals or groups of decision makers (including autonomous agents) would be utilised on the basis of their capability within the requirements of the scenario to collaboratively solve the decision problem. Features of the scenario such as time criticality, required experience level, the need for justification, and conflict management, will be addressed within the architecture to ensure that the most appropriate decision management support (system/naturalistic/hybrid) is provided. In addition to being reliant on a number of human factors issues, the decision making process is also reliant on a number of information issues: overload, consistency, completeness, uncertainty and evolution, which will be discussed within the context of the architecture
Prediction and Situational Option Generation in Soccer
Paul Ward, Michigan Technological University
Naturalistic models of decision making, such as the Recognition-
Primed Decision (RPD) model (e.g., Klein, Calderwood, &
Clinton-Cirocco, 1986; Klein, 1997), suggest that as individuals
become more experienced within a domain they automatically
recognize situational patterns as familiar which, in turn, activates
an associated situational response. Typically, this results in a
workable course of action being generated first, and subsequent
options generated only if the initial option proves ineffective
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
For a company looking to provide delightful user experiences, it is of
paramount importance to take care of any customer issues. This paper proposes
COTA, a system to improve speed and reliability of customer support for end
users through automated ticket classification and answers selection for support
representatives. Two machine learning and natural language processing
techniques are demonstrated: one relying on feature engineering (COTA v1) and
the other exploiting raw signals through deep learning architectures (COTA v2).
COTA v1 employs a new approach that converts the multi-classification task into
a ranking problem, demonstrating significantly better performance in the case
of thousands of classes. For COTA v2, we propose an Encoder-Combiner-Decoder, a
novel deep learning architecture that allows for heterogeneous input and output
feature types and injection of prior knowledge through network architecture
choices. This paper compares these models and their variants on the task of
ticket classification and answer selection, showing model COTA v2 outperforms
COTA v1, and analyzes their inner workings and shortcomings. Finally, an A/B
test is conducted in a production setting validating the real-world impact of
COTA in reducing issue resolution time by 10 percent without reducing customer
satisfaction
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