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Conversational Help for Task Completion and Feature Discovery in Personal Assistants
Our system comprises of a C-BiLSTM based classifier, which is a fusion of Convolutional Neural Networks (CNN) and Bidirectional LSTM (BiLSTM) architectures, to detect help queries and a semantic Approximate Nearest Neighbours (ANN) module to map the query to an appropriate predefined response.
An Unsupervised Approach for Mapping between Vector Spaces
Our model handles the problem of data scarcity which is faced by many languages in the world and yields improved word embeddings for words in the target language by relying on transformed embeddings of words of the source language.
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