Recommendations for Systematic Research on Emergent Language

• URL: http://arxiv.org/abs/2206.11302v1
• Date: Wed, 22 Jun 2022 18:10:44 GMT
• Title: Recommendations for Systematic Research on Emergent Language
• Authors: Brendon Boldt, David Mortensen
• Abstract summary: We identify the overarching goals of emergent language research, categorizing them as either science or engineering. We present core methodological elements of science and engineering, analyze their role in current emergent language research, and recommend how to apply these elements.
• Score: 1.52292571922932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emergent language is unique among fields within the discipline of machine learning for its open-endedness, not obviously presenting well-defined problems to be solved. As a result, the current research in the field has largely been exploratory: focusing on establishing new problems, techniques, and phenomena. Yet after these problems have been established, subsequent progress requires research which can measurably demonstrate how it improves on prior approaches. This type of research is what we call systematic research; in this paper, we illustrate this mode of research specifically for emergent language. We first identify the overarching goals of emergent language research, categorizing them as either science or engineering. Using this distinction, we present core methodological elements of science and engineering, analyze their role in current emergent language research, and recommend how to apply these elements.

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)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Teaching Research Design in Software Engineering [1.9659095632676098]
  Empirical Software Engineering (ESE) has emerged as a contending force aiming to critically evaluate and provide knowledge that informs practice in adopting new technologies. This chapter teaches foundational skills in research design, essential for educating software engineers and researchers in ESE.
  arXiv  Detail & Related papers  (2024-07-06T21:06:13Z)
• A Review of the Applications of Deep Learning-Based Emergent Communication [1.6574413179773761]
  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.
  arXiv  Detail & Related papers  (2024-07-03T17:43:54Z)
• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• Language Evolution with Deep Learning [49.879239655532324]
  Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models.
  arXiv  Detail & Related papers  (2024-03-18T16:52:54Z)
• Parsing Objects at a Finer Granularity: A Survey [54.72819146263311]
  Fine-grained visual parsing is important in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms. We conduct an in-depth study of the advanced work from a new perspective of learning the part relationship.
  arXiv  Detail & Related papers  (2022-12-28T04:20:10Z)
• 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)
• The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles [0.0]
  We present a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO) The CSO takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.
  arXiv  Detail & Related papers  (2021-04-02T09:02:32Z)
• A New Neural Search and Insights Platform for Navigating and Organizing AI Research [56.65232007953311]
  We introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.
  arXiv  Detail & Related papers  (2020-10-30T19:12:25Z)
• 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)

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.