We describe an automated assistant for answering frequently asked questions; our system has been deployed, and is currently answering HR-related queries in two different areas (leave management and health insurance) to a large number of users. The needs of a large global corporate lead us to model a frequently asked question (FAQ) to be an equivalence class of actually asked questions, for which there is a common answer (certified as being consistent with the organization's policy). When a new question is posed to our system, it finds the class of question, and responds with the answer for the class. At this point, the system is either correct (gives correct answer); or incorrect (gives wrong answer); or incomplete (says "I don't know''). We employ a hybrid deep-learning architecture in which a BiLSTM-based classifier is combined with second BiLSTM-based Siamese network in an iterative manner: Questions for which the classifier makes an error during training are used to generate a set of misclassified question-question pairs. These, along with correct pairs, are used to train the Siamese network to drive apart the (hidden) representations of the misclassified pairs. We present experimental results from our deployment showing that our iteratively trained hybrid network: (a) results in better performance than using just a classifier network, or just a Siamese network; (b) performs better than state-of-the art sentence classifiers in the two areas in which it has been deployed, in terms of both accuracy as well as precision-recall tradeoff; and (c) also performs well on a benchmark public dataset. We also observe that using question-question pairs in our hybrid network, results in marginally better performance than using question-to-answer pairs. Finally, estimates of precision and recall from the deployment of our automated assistant suggest that we can expect the burden on our HR department to drop from answering about 6000 queries a day to about 1000.

Modern industrial equipments of all kinds are instrumented with a large number of sensors that continuously transmit their readings wirelessly, giving rise to what is often referred to as the `industrial internet'. Such data are often explored by engineers to determine the different usage patterns and behavior of similar machines. In this paper we describe a technique to automatically summarize the usage and behavioral patterns of a collection of similar machines by a small set of rules that nevertheless cover a large fraction of the observed data. We characterize the usage and behavior of a machine over a day, by a collection of single-sensor histograms; thus each day is a point in a high-dimensional space. We first cluster days according to each sensor separately and then combine the clusters using communities in a specially constructed graph that considers common days within clusters of different sensors. In the process some clusters of a single sensor get merged. Finally, we discover rules, each comprising of memberships in clusters of possibly different sensors. Thus, we use the term multi-subspace clustering to describe such a collection of cluster-based rules. Last but not the least, we attempt to cover a large fraction of observed days with a small number of such rules. We present empirical results on voluminous (100s of GBs) real-life sensor data and also compare our technique with related work in subspace clustering and histogram summarization.

This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CProgol is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). Performance is measured using both predictive accuracy and a new cost function, Relative Advantage ( RA ). The RA results show that searching for NPPs by using our best NPP predictor as a filter is more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. Predictive accuracy is not a good measure of performance for this domain because it does not discriminate well between NPP recognition models: despite covering varying numbers of (the rare) positives, all the models are awarded a similar (high) score by predictive accuracy because they all exclude most of the abundant negatives.