A Review of the Applications of Deep Learning-Based Emergent Communication
- URL: http://arxiv.org/abs/2407.03302v1
- Date: Wed, 3 Jul 2024 17:43:54 GMT
- Title: A Review of the Applications of Deep Learning-Based Emergent Communication
- Authors: Brendon Boldt, David Mortensen,
- Abstract summary: Emergent communication, or emergent language, is the field of research which studies how human language-like communication systems emerge de novo in deep reinforcement learning environments.
This paper comprehensively reviews the applications of emergent communication research across machine learning, natural language processing, linguistics, and cognitive science.
- Score: 1.6574413179773761
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Emergent communication, or emergent language, is the field of research which studies how human language-like communication systems emerge de novo in deep multi-agent reinforcement learning environments. The possibilities of replicating the emergence of a complex behavior like language have strong intuitive appeal, yet it is necessary to complement this with clear notions of how such research can be applicable to other fields of science, technology, and engineering. This paper comprehensively reviews the applications of emergent communication research across machine learning, natural language processing, linguistics, and cognitive science. Each application is illustrated with a description of its scope, an explication of emergent communication's unique role in addressing it, a summary of the extant literature working towards the application, and brief recommendations for near-term research directions.
Related papers
- A Survey on Emergent Language [9.823821010022932]
The paper provides a comprehensive review of 181 scientific publications on emergent language in artificial intelligence.
Its objective is to serve as a reference for researchers interested in or proficient in the field.
arXiv Detail & Related papers (2024-09-04T12:22:05Z) - Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components [4.262342157729123]
This study investigates the links between theoretical studies and technical implementations of conversational search systems.
We present a layered architecture framework and explain the core functions of conversational search systems.
We reflect on our findings in light of the rapid progress in large language models, discussing their capabilities, limitations, and directions for future research.
arXiv Detail & Related papers (2024-07-01T06:24:11Z) - Addressing the Blind Spots in Spoken Language Processing [4.626189039960495]
We argue that understanding human communication requires a more holistic approach that goes beyond textual or spoken words to include non-verbal elements.
We propose the development of universal automatic gesture segmentation and transcription models to transcribe these non-verbal cues into textual form.
arXiv Detail & Related papers (2023-09-06T10:29:25Z) - Large Language Models for Information Retrieval: A Survey [58.30439850203101]
Information retrieval has evolved from term-based methods to its integration with advanced neural models.
Recent research has sought to leverage large language models (LLMs) to improve IR systems.
We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
arXiv Detail & Related papers (2023-08-14T12:47:22Z) - Interactive Natural Language Processing [67.87925315773924]
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP.
This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
arXiv Detail & Related papers (2023-05-22T17:18:29Z) - An Inclusive Notion of Text [69.36678873492373]
We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP.
We introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling.
arXiv Detail & Related papers (2022-11-10T14:26:43Z) - Neural Approaches to Conversational Information Retrieval [94.77863916314979]
A conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface.
Recent progress in deep learning has brought tremendous improvements in natural language processing (NLP) and conversational AI.
This book surveys recent advances in CIR, focusing on neural approaches that have been developed in the last few years.
arXiv Detail & Related papers (2022-01-13T19:04:59Z) - Software-Based Dialogue Systems: Survey, Taxonomy and Challenges [4.2763155274587366]
This paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies.
As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents' field.
arXiv Detail & Related papers (2021-06-21T07:41:44Z) - Positioning yourself in the maze of Neural Text Generation: A
Task-Agnostic Survey [54.34370423151014]
This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc.
We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
arXiv Detail & Related papers (2020-10-14T17:54:42Z) - Experience Grounds Language [185.73483760454454]
Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates.
Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world.
arXiv Detail & Related papers (2020-04-21T16:56:27Z)
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.