Comparing Approaches to Language Understanding for Human-Robot Dialogue: An Error Taxonomy and Analysis
Comparing Approaches to Language Understanding for Human-Robot Dialogue: An Error Taxonomy and Analysis
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发表时间：
2022
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通讯作者：
A. D. Tur;D. Traum
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作者：
A. D. Tur;D. Traum
在本文中，我们比较了人机交互领域的两种不同的语言理解方法，其中人类指挥官向机器人发出导航指令。我们将基于相关性的分类器与 GPT-2 模型进行对比，使用大约 2000 个输入输出示例作为训练数据。在这种级别的训练数据下，基于相关性的模型比基于 GPT-2 的模型性能好 79% 到 8%。我们还对每个模型所犯的错误类型进行了分类，表明它们具有不同的优点和缺点，因此我们还研究了组合模型的潜力。
In this paper, we compare two different approaches to language understanding for a human-robot interaction domain in which a human commander gives navigation instructions to a robot. We contrast a relevance-based classifier with a GPT-2 model, using about 2000 input-output examples as training data. With this level of training data, the relevance-based model outperforms the GPT-2 based model 79% to 8%. We also present a taxonomy of types of errors made by each model, indicating that they have somewhat different strengths and weaknesses, so we also examine the potential for a combined model.