Efforts towards endowing robots with the ability to speak have benefited from recent advancements in NLP, in particular large language models. However, as powerful as current models have become, they still operate on sentence or multi-sentence level input, not on the word-by-word input that humans operate on, affecting the degree of responsiveness that they offer, which is critical in situations where humans interact with robots using speech. 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.

本研究解决了现有对话系统在响应性上的不足，尤其是机器人与人类的互动中对增量对话管理的需求。提出了一种新颖的方法，系统回顾了现有的增量模型，包括语音识别和语言生成，并强调了对话管理器在其中的重要性。研究发现，增量对话管理的研究相对较少，提出了一些实用要求，并探讨了其对具身机器人平台的潜在影响。

增量对话管理：调查、讨论及其对人机交互的影响