Visible to the public Attention-based Sequential Generative Conversational Agent

TitleAttention-based Sequential Generative Conversational Agent
Publication TypeConference Paper
Year of Publication2020
AuthorsKumar, Nripesh, Srinath, G., Prataap, Abhishek, Nirmala, S. Jaya
Conference Name2020 5th International Conference on Computing, Communication and Security (ICCCS)
Date Publishedoct
KeywordsAdversarial training, Attention, Complexity theory, Computer science, conversational agent, conversational agents, Decoding, entailment, Generators, Human Behavior, LSTM, Metrics, pubcrawl, Scalability, Task Analysis, Term Frequency –Inverse Document Frequency(TF-IDF), Training, Vocabulary
AbstractIn this work, we examine the method of enabling computers to understand human interaction by constructing a generative conversational agent. An experimental approach in trying to apply the techniques of natural language processing using recurrent neural networks (RNNs) to emulate the concept of textual entailment or human reasoning is presented. To achieve this functionality, our experiment involves developing an integrated Long Short-Term Memory cell neural network (LSTM) system enhanced with an attention mechanism. The results achieved by the model are shown in terms of the number of epochs versus loss graphs as well as a brief illustration of the model's conversational capabilities.
DOI10.1109/ICCCS49678.2020.9277360
Citation Keykumar_attention-based_2020