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arXiv · 2112.05843

Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity

Kurt Shuster·Jack Urbanek·Arthur Szlam·Jason Weston
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Abstract

State-of-the-art dialogue models still often stumble with regards to factual accuracy and self-contradiction. Anecdotally, they have been observed to fail to maintain character identity throughout discourse; and more specifically, may take on the role of their interlocutor. In this work we formalize and quantify this deficiency, and show experimentally through human evaluations that this is indeed a problem. In contrast, we show that discriminative models trained specifically to recognize who is speaking can perform well; and further, these can be used as automated metrics. Finally, we evaluate a wide variety of mitigation methods, including changes to model architecture, training protocol, and decoding strategy. Our best models reduce mistaken identity issues by nearly 65% according to human annotators, while simultaneously improving engagingness. Despite these results, we find that maintaining character identity still remains a challenging problem.

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Kurt Shuster, Jack Urbanek, Arthur Szlam, Jason Weston. 2021-12-10. Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity. https://arxiv.org/abs/2112.05843

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