Hu Qian, Mohamed Thahir, Xiao Wei, Ma Xiyao, Gao Xibin, Gao Zheng, Arava Radhika, AbdelHady Mohamed
Amazon Alexa AI, Seattle, WA, United States.
Front Big Data. 2022 Apr 25;5:867251. doi: 10.3389/fdata.2022.867251. eCollection 2022.
Intelligent personal assistants (IPAs) such as Amazon Alexa, Google Assistant and Apple Siri extend their built-in capabilities by supporting voice apps developed by third-party developers. Sometimes the smart assistant is not able to successfully respond to user voice commands (aka utterances). There are many reasons including automatic speech recognition (ASR) error, natural language understanding (NLU) error, routing utterances to an irrelevant voice app, or simply that the user is asking for a capability that is not supported yet. The failure to handle a voice command leads to customer frustration. In this article, we introduce a fallback skill recommendation system (FROST) to suggest a voice app to a customer for an unhandled voice command. There are several practical issues when developing a skill recommender system for IPAs, i.e., partial observation, hard and noisy utterances. To solve the partial observation problem, we propose collaborative data relabeling (CDR) method. To mitigate hard and noisy utterance issues, we propose a rephrase-based relabeling technique. We evaluate the proposed system in both offline and online settings. The offline evaluation results show that the FROST system outperforms the baseline rule-based system. The online A/B testing results show a significant gain of customer experience metrics.
诸如亚马逊Alexa、谷歌助手和苹果Siri等智能个人助理（IPA）通过支持第三方开发者开发的语音应用程序来扩展其内置功能。有时，智能助手无法成功响应用户语音命令（即话语）。原因有很多，包括自动语音识别（ASR）错误、自然语言理解（NLU）错误、将话语路由到不相关的语音应用程序，或者仅仅是用户要求的功能尚未得到支持。无法处理语音命令会导致客户沮丧。在本文中，我们介绍了一种备用技能推荐系统（FROST），用于为未处理的语音命令向客户推荐语音应用程序。为IPA开发技能推荐系统时存在几个实际问题，即部分观察、硬话语和噪声话语。为了解决部分观察问题，我们提出了协作数据重新标记（CDR）方法。为了缓解硬话语和噪声话语问题，我们提出了一种基于改写的重新标记技术。我们在离线和在线设置中评估了所提出的系统。离线评估结果表明，FROST系统优于基于规则的基线系统。在线A/B测试结果显示客户体验指标有显著提升。
