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arXiv · 1902.08654

What makes a good conversation? How controllable attributes affect human judgments

Abigail See·Stephen Roller·Douwe Kiela·Jason Weston
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Abstract

A good conversation requires balance -- between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly evaluated via human judgments of overall quality, the relationship between quality and these individual factors is less well-studied. In this work, we examine two controllable neural text generation methods, conditional training and weighted decoding, in order to control four important attributes for chitchat dialogue: repetition, specificity, response-relatedness and question-asking. We conduct a large-scale human evaluation to measure the effect of these control parameters on multi-turn interactive conversations on the PersonaChat task. We provide a detailed analysis of their relationship to high-level aspects of conversation, and show that by controlling combinations of these variables our models obtain clear improvements in human quality judgments.

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Abigail See, Stephen Roller, Douwe Kiela, Jason Weston. 2019-02-22. What makes a good conversation? How controllable attributes affect human judgments. https://arxiv.org/abs/1902.08654

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