Incremental Dialogue Management: Survey, Discussion, and Implications for HRI
- URL: http://arxiv.org/abs/2501.00953v1
- Date: Wed, 01 Jan 2025 20:58:03 GMT
- Title: Incremental Dialogue Management: Survey, Discussion, and Implications for HRI
- Authors: Casey Kennington, Pierre Lison, David Schlangen,
- Abstract summary: 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.
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.
- Score: 16.34485107181007
- License:
- Abstract: 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.
Related papers
- WavChat: A Survey of Spoken Dialogue Models [66.82775211793547]
Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain.
These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech.
Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems.
arXiv Detail & Related papers (2024-11-15T04:16:45Z) - A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation [37.79563028123686]
In open domain multi turn dialogue generation, it is essential to modeling the contextual semantics of the dialogue history.
Previous research had verified the effectiveness of the hierarchical recurrent encoder-decoder framework on open domain multi turn dialogue generation.
We propose a static and dynamic attention-based approach to model the dialogue history and then generate open domain multi turn dialogue responses.
arXiv Detail & Related papers (2024-10-28T06:05:34Z) - I Was Blind but Now I See: Implementing Vision-Enabled Dialogue in
Social Robots [0.040792653193642496]
This paper presents an initial implementation of a dialogue manager that enhances the traditional text-based prompts with real-time visual input.
The system's prompt engineering, incorporating dialogue with summarisation of the images, ensures a balance between context preservation and computational efficiency.
arXiv Detail & Related papers (2023-11-15T13:47:00Z) - Are cascade dialogue state tracking models speaking out of turn in
spoken dialogues? [1.786898113631979]
This paper proposes a comprehensive analysis of the errors of state of the art systems in complex settings such as Dialogue State Tracking.
Based on spoken MultiWoz, we identify that errors on non-categorical slots' values are essential to address in order to bridge the gap between spoken and chat-based dialogue systems.
arXiv Detail & Related papers (2023-11-03T08:45:22Z) - A Review of Dialogue Systems: From Trained Monkeys to Stochastic Parrots [0.0]
We aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans.
We present a broad overview of methods developed to build dialogue systems over the years.
arXiv Detail & Related papers (2021-11-02T08:07:55Z) - "How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken
Conversations [87.95711406978157]
This work presents a new benchmark on spoken task-oriented conversations.
We study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling.
Our data set enables speech-based benchmarking of task-oriented dialogue systems.
arXiv Detail & Related papers (2021-09-28T04:51:04Z) - Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
We review the previous methods from the perspective of dialogue modeling.
We discuss three typical patterns of dialogue modeling that are widely-used in dialogue comprehension tasks.
arXiv Detail & Related papers (2021-03-04T15:50:17Z) - TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented
Dialogue [113.45485470103762]
In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling.
To better model dialogue behavior during pre-training, we incorporate user and system tokens into the masked language modeling.
arXiv Detail & Related papers (2020-04-15T04:09:05Z) - You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation.
Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
arXiv Detail & Related papers (2020-04-11T12:51:07Z) - Recent Advances and Challenges in Task-oriented Dialog System [63.82055978899631]
Task-oriented dialog systems are attracting more and more attention in academic and industrial communities.
We discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning, and (3) integrating domain knowledge into the dialog model.
arXiv Detail & Related papers (2020-03-17T01:34:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.