CloneBot: Personalized Dialogue-Response Predictions

• URL: http://arxiv.org/abs/2103.16750v1
• Date: Wed, 31 Mar 2021 01:15:37 GMT
• Title: CloneBot: Personalized Dialogue-Response Predictions
• Authors: Tyler Weitzman and Hoon Pyo (Tim) Jeon
• Abstract summary: The project task was to create a model that, given a speaker ID, chat history, and an utterance query, can predict the response utterance in a conversation. The model is personalized for each speaker. This task can be a useful tool for building speech bots that talk in a human-like manner in a live conversation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our project task was to create a model that, given a speaker ID, chat history, and an utterance query, can predict the response utterance in a conversation. The model is personalized for each speaker. This task can be a useful tool for building speech bots that talk in a human-like manner in a live conversation. Further, we succeeded at using dense-vector encoding clustering to be able to retrieve relevant historical dialogue context, a useful strategy for overcoming the input limitations of neural-based models when predictions require longer-term references from the dialogue history. In this paper, we have implemented a state-of-the-art model using pre-training and fine-tuning techniques built on transformer architecture and multi-headed attention blocks for the Switchboard corpus. We also show how efficient vector clustering algorithms can be used for real-time utterance predictions that require no training and therefore work on offline and encrypted message histories.

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