Proto: A Neural Cocktail for Generating Appealing Conversations
Sougata Saha, Souvik Das, Elizabeth Soper, Erin Pacquetet, Rohini K., Srihari

TL;DR
Proto is a sophisticated neural socialbot that combines diverse knowledge sources, neural and rule-based modules, and innovative strategies to generate engaging, coherent, and factual conversations for a memorable user experience.
Contribution
This paper introduces Proto, a novel socialbot integrating neural, rule-based, and deterministic components for improved conversational quality and consistency.
Findings
Achieved consistent high scores in the Alexa Prize competition.
Generated responses that are colloquial, empathetic, and on-topic.
Enhanced factual correctness and self-rectification in conversations.
Abstract
In this paper, we present our Alexa Prize Grand Challenge 4 socialbot: Proto. Leveraging diverse sources of world knowledge, and powered by a suite of neural and rule-based natural language understanding modules, state-of-the-art neural generators, novel state-based deterministic generators, an ensemble of neural re-rankers, a robust post-processing algorithm, and an efficient overall conversation strategy, Proto strives to be able to converse coherently about a diverse range of topics of interest to humans, and provide a memorable experience to the user. In this paper we dissect and analyze the different components and conversation strategies implemented by our socialbot, which enables us to generate colloquial, empathetic, engaging, self-rectifying, factually correct, and on-topic response, which has helped us achieve consistent scores throughout the competition.
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
