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Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions

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arxiv 2006.12442 v2 pith:A75V3R6J submitted 2020-06-22 cs.CL cs.AI

Stephen Roller , Y-Lan Boureau , Jason Weston , Antoine Bordes , Emily Dinan , Angela Fan , David Gunning , Da Ju
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Margaret Li Spencer Poff Pratik Ringshia Kurt Shuster Eric Michael Smith Arthur Szlam Jack Urbanek Mary Williamson
This is my paper · ORCID
classification cs.CLcs.AI
keywords agentconversationalengagingopen-domainprovidingviewworkagents
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We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet. We present a biased view, focusing on work done by our own group, while citing related work in each area. In particular, we discuss in detail the properties of continual learning, providing engaging content, and being well-behaved -- and how to measure success in providing them. We end with a discussion of our experience and learnings, and our recommendations to the community.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A comprehensive reanalysis finds that min-p sampling does not outperform top-p, top-k, or basic sampling once the original data are re-tested and hyperparameter budgets are equalized.

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