让机器人对话具备实时修正能力，提升人机交互自然性。
Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Models
- 提出增量式对话管理框架，支持逐词动态调整
- 发现现有研究中增量对话管理严重不足
- 适合关注机器人交互与大模型融合的开发者
赋予机器人语言能力得益于自然语言处理的进步，特别是大语言模型的发展。然而，当前语言模型并非完全增量式，其处理过程本质上是单向的，无法根据新信息修正理解或输出结果。这种单向性对人机对话系统开发有重要影响。本文回顾了在单词级或更低粒度上实现增量运行的交互系统文献，阐明了构建增量系统的必要性，并综述了语音识别、语言生成等对话关键环节的增量建模进展。重点聚焦于决策模块——对话管理器。研究发现增量对话管理相关研究极为有限，提出了实用增量对话管理的需求与设计原则，并探讨了增量对话在大语言模型时代对具身机器人平台的影响。
原文摘要 · Abstract (English)
Efforts towards endowing robots with the ability to speak have benefited from recent advancements in natural language processing, in particular large language models. However, current language models are not fully incremental, as their processing is inherently monotonic and thus lack the ability to revise their interpretations or output in light of newer observations. This monotonicity has important implications for the development of dialogue systems for human--robot interaction. In this paper, we review the literature on interactive systems that operate incrementally (i.e., at the word level or below it). We motivate the need for incremental systems, survey incremental modeling of important aspects of dialogue like speech recognition and language generation. Primary focus is on the part of the system that makes decisions, known as the dialogue manager. We find that there is very little research on incremental dialogue management, offer some requirements for practical incremental dialogue management, and the implications of incremental dialogue for embodied, robotic platforms in the age of large language models.
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