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The problem of building a coherent and non-monotonous conversational agentwith proper discourse and coverage is still an area of open research. Currentarchitectures only take care of semantic and contextual information for a givenquery and fail to completely account for syntactic and external knowledge whichare crucial for generating responses in a chit-chat system. To overcome thisproblem, we propose an end to end multi-stream deep learning architecture whichlearns unified embeddings for query-response pairs by leveraging contextualinformation from memory networks and syntactic information by incorporatingGraph Convolution Networks (GCN) over their dependency parse. A stream of thisnetwork also utilizes transfer learning by pre-training a bidirectionaltransformer to extract semantic representation for each input sentence andincorporates external knowledge through the the neighborhood of the entitiesfrom a Knowledge Base (KB). We benchmark these embeddings on next sentenceprediction task and significantly improve upon the existing techniques.Furthermore, we use AMUSED to represent query and responses along with itscontext to develop a retrieval based conversational agent which has beenvalidated by expert linguists to have comprehensive engagement with humans.

AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue