Helping Chatbots To Better Understand User Requests Efficiently Using Human Computation

Chatbots are the text based conversational agents with which users interact in natural language. They are becoming more and more popular with the immense growth in messaging apps and tools to develop text based conversational agents. Despite of advances in Artificial Intelligence and Natural Language Processing, chatbots still struggle in accurately understanding user requests, thus providing wrong answers or no response. An effective solution to tackle this problem is involving human's capabilities in chatbot’s operations for understanding user requests. There are many existing systems using humans in chatbots but they are not capable to scale up with the increasing number of users. To address this problem, we provide insights in how to design such chatbot system having humans in the loop and how to involve humans efficiently.We perform an extensive literature survey about chatbots, and human computation applied for a chatbot, to guide the design of our reference chatbot system. Then we address the problem of cold starting chatbot systems. We propose a methodology to generate high quality training data, with which, chatbot’s Natural Language Understanding (NLU) model can be trained, making a chatbot capable of handling user requests efficiently at run time. Finally we provide a methodology to estimate the reliability of black box NLU models based on the confidence threshold of their prediction functionality. We study and discuss the effect of parameters such as training data set size, type of intents on automatic NLU model.<br/

Effective crowdsourced generation of training data for chatbots natural language understanding

Chatbots are text-based conversational agents. Natural Language Understanding (NLU) models are used to extract meaning and intention from user messages sent to chatbots. The user experience of chatbots largely depends on the performance of the NLU model, which itself largely depends on the initial dataset the model is trained with. The training data should cover the diversity of real user requests the chatbot will receive. Obtaining such data is a challenging task even for big corporations. We introduce a generic approach to generate training data with the help of crowd workers, we discuss the approach workflow and the design of crowdsourcing tasks assuring high quality. We evaluate the approach by running an experiment collecting data for 9 different intents. We use the collected training data to train a natural language understanding model. We analyse the performance of the model under different training set sizes for each intent. We provide recommendations on selecting an optimal confidence threshold for predicting intents, based on the cost model of incorrect and unknown predictions

Effective crowdsourced generation of training data for chatbots natural language understanding

Chatbots are text-based conversational agents. Natural Language Understanding (NLU) models are used to extract meaning and intention from user messages sent to chatbots. The user experience of chatbots largely depends on the performance of the NLU model, which itself largely depends on the initial dataset the model is trained with. The training data should cover the diversity of real user requests the chatbot will receive. Obtaining such data is a challenging task even for big corporations. We introduce a generic approach to generate training data with the help of crowd workers, we discuss the approach workflow and the design of crowdsourcing tasks assuring high quality. We evaluate the approach by running an experiment collecting data for 9 different intents. We use the collected training data to train a natural language understanding model. We analyse the performance of the model under different training set sizes for each intent. We provide recommendations on selecting an optimal confidence threshold for predicting intents, based on the cost model of incorrect and unknown predictions.Accepted Author ManuscriptWeb Information System

Ameloblastin Binds to Phospholipid Bilayers via a Helix-Forming Motif within the Sequence Encoded by Exon 5

Ameloblastin (Ambn), the most abundant non-amelogenin enamel protein, is intrinsically disordered and has the potential to interact with other enamel proteins and with cell membranes. Here, through multiple biophysical methods, we investigated the interactions between Ambn and large unilamellar vesicles (LUVs), whose lipid compositions mimicked cell membranes involved in epithelial cell-extracellular matrix adhesion. Using a series of Ambn Trp/Phe variants and Ambn mutants, we further showed that Ambn binds to LUVs through a highly conserved motif within the sequence encoded by exon 5. Synthetic peptides derived from different regions of Ambn confirmed that the sequence encoded by exon 5 is involved in LUV binding. Sequence analysis of Ambn across different species showed that the N-terminus of this sequence contains a highly conserved motif with a propensity to form an amphipathic helix. Mutations in the helix-forming sequence resulted in a loss of peptide binding to LUVs. Our in vitro data suggest that Ambn binds the lipid membrane directly through a conserved helical motif and have implications for biological events such as Ambn-cell interactions, Ambn signaling, and Ambn secretion via secretory vesicles

Modeling and analysis of a printed circuit heat exchanger for supercritical CO2 power cycle applications

The supercritical carbon dioxide (S-CO2) based Brayton cycle is a good alternative to conventional power cycles because of high cycle efficiency, compact turbo machinery and compact heat exchangers. In this cycle, the majority of heat transfer (approximately 60-70% of total cycle heat transfer) occurs in the regenerator. For the regenerator, micro-channel heat exchanger is an attractive option because of its high surface-area-to-volume ratio. In this study, the performance of a printed circuit heat exchanger (PCHE) with straight and zigzag channels is evaluated. The study is performed for fully turbulent conditions. The channel diameter and the operating Reynolds number play significant roles in the overall heat transfer and pressure drop of hot and cold channels of S-CO2. For zigzag channels, it is found that a larger bend angle and smaller linear pitch perform better than a smaller bend angle and large linear pitch combination. Correlations for Nusselt number and friction factor are developed using ANSYS Fluent and are subsequently utilized in one dimensional (1D) thermal modeling of the heat exchanger. For the same thermal capacity, the model indicates that the zigzag channel PCHE volume is significantly smaller than that of a straight channel PCHE because of higher heat transfer coefficient. However, the pressure drop incurred in the former design is larger. (C) 2016 Elsevier Ltd. All rights reserved