论文标题
与自然语言推断对话的语义多样性
Semantic Diversity in Dialogue with Natural Language Inference
论文作者
论文摘要
对于神经对话剂而言，产生对聊天对话的各种有趣的反应是一个问题。本文为改善对话生成的多样性做出了两项实质性贡献。首先，我们提出了一种新颖的指标，该指标使用自然语言推断（NLI）来衡量一组对话的模型响应的语义多样性。我们使用既定框架（Tevet and Berant，2021）评估该指标，并找到有力的证据，表明NLI多样性与语义多样性相关。具体而言，我们表明，矛盾关系比测量这种多样性的中性关系更有用，并且融合了NLI模型的信心可实现最新的结果。其次，我们演示了如何通过称为多样性阈值生成的新一代程序来迭代改善一组响应的语义多样性，这与标准生成程序相比，NLI多样性平均增加了137％。
Generating diverse, interesting responses to chitchat conversations is a problem for neural conversational agents. This paper makes two substantial contributions to improving diversity in dialogue generation. First, we propose a novel metric which uses Natural Language Inference (NLI) to measure the semantic diversity of a set of model responses for a conversation. We evaluate this metric using an established framework (Tevet and Berant, 2021) and find strong evidence indicating NLI Diversity is correlated with semantic diversity. Specifically, we show that the contradiction relation is more useful than the neutral relation for measuring this diversity and that incorporating the NLI model's confidence achieves state-of-the-art results. Second, we demonstrate how to iteratively improve the semantic diversity of a sampled set of responses via a new generation procedure called Diversity Threshold Generation, which results in an average 137% increase in NLI Diversity compared to standard generation procedures.
