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

Reply With: Proactive Recommendation of Email Attachments

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 201

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

Finding Relevant Answers in Software Forums

Abstract—Online software forums provide a huge amount of valuable content. Developers and users often ask questions and receive answers from such forums. The availability of a vast amount of thread discussions in forums provides ample opportunities for knowledge acquisition and summarization. For a given search query, current search engines use traditional information retrieval approach to extract webpages containin

The 2014 American State Litter Scorecard FINAL: USA's Dirtiest & Cleanest States Includes Statistics and Charts

A NEW State Litter "Scorecard" is released for the 2014 American Society for Public Administration (ASPA) Conference. Every three years, the Scorecard approximates each state's overall public spaces environmental quality through tried-and-true, hard-to-publicly obtain objective and subjective measures, resulting in a total overall jurisdictional score. Readers gain a realistic "picture" of "what's going on" within one or all of the 50 states. Illegal littering and dumping, found frequently on or near transportation paths, creates danger to public safety and health, with 800+ Americans dying each year by vehicle collisions with unmoved roadway debris. Because policy makers, public administrators and citizens are ever more involved in effectuating "green" outcomes, satisfactory public spaces waste removals are vital. Since 2008, major publications (the Boston Globe; TRAVEL+LEISURE; National Cooperative Highway Research Program's "Reducing Litter on Roadsides" Journal) have referred to the Scorecard, an ever valuable, trusted standard for improving debris/litter abatement in states and localities

The 2014 American State Litter Scorecard FINAL: USA's Dirtiest & Cleanest States Includes Statistics and Charts

A NEW State Litter "Scorecard" is released for the 2014 American Society for Public Administration (ASPA) Conference. Every three years, the Scorecard approximates each state's overall public spaces environmental quality through tried-and-true, hard-to-publicly obtain objective and subjective measures, resulting in a total overall jurisdictional score. Readers gain a realistic "picture" of "what's going on" within one or all of the 50 states. Illegal littering and dumping, found frequently on or near transportation paths, creates danger to public safety and health, with 800+ Americans dying each year by vehicle collisions with unmoved roadway debris. Because policy makers, public administrators and citizens are ever more involved in effectuating "green" outcomes, satisfactory public spaces waste removals are vital. Since 2008, major publications (the Boston Globe; TRAVEL+LEISURE; National Cooperative Highway Research Program's "Reducing Litter on Roadsides" Journal) have referred to the Scorecard, an ever valuable, trusted standard for improving debris/litter abatement in states and localities

Determining the polarity of postings for discussion search

When performing discussion search it might be desirable to consider non-topical measures like the number of positive and negative replies to a posting, for instance as one possible indicator for the trustworthiness of a comment. Systems like POLAR are able to integrate such values into the retrieval function. To automatically detect the polarity of postings, they need to be classified into positive and negative ones w.r.t.\ the comment or document they are annotating. We present a machine learning approach for polarity detection which is based on Support Vector Machines. We discuss and identify appropriate term and context features. Experiments with ZDNet News show that an accuracy of around 79\%-80\% can be achieved for automatically classifying comments according to their polarity