鲸类翻译倡议：破译抹香鲸通信的路线图
Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales
摘要：
在过去的十年里，机器学习在人类语言分析中取得了突破性的发展，目前的方法能够从大型数据汇集中自动准确地恢复语法和语义的各个方面，包括句子结构和基础词义。最近的研究表明，此类工具在分析非人类物种的声学通信方面有希望。我们认为，机器学习将是未来收集、处理和分析动物交流研究中多模态数据流的基石，包括生物声学、行为、生物学和环境数据。鲸目动物是独特的非人类模式物种，因为它们拥有复杂的声学通信，但使用一种非常不同的编码系统，该系统在水生而非陆地介质中进化。特别是抹香鲸，它们具有高度发达的神经解剖学特征、认知能力、社会结构和离散的基于点击的编码，这为先进的机器学习工具提供了一个很好的起点，这些工具可以在未来应用于其他动物。本文详细介绍了基于现有技术和多学科科学界努力实现这一目标的路线图。我们概述了收集和处理抹香鲸大量生物声学数据所需的关键要素，检测它们的基本通信单元和类似语言的高级结构，并通过交互式回放实验验证这些模型。这样一项事业所开发的技术能力可能会在更广泛的社区调查非人类交流和动物行为研究中产生交叉应用和进步。
英文摘要：
The past decade has witnessed a groundbreaking rise of machine learning for human language analysis, with current methods capable of automatically accurately recovering various aspects of syntax and semantics - including sentence structure and grounded word meaning - from large data collections. Recent research showed the promise of such tools for analyzing acoustic communication in nonhuman species. We posit that machine learning will be the cornerstone of future collection, processing, and analysis of multimodal streams of data in animal communication studies, including bioacoustic, behavioral, biological, and environmental data. Cetaceans are unique non-human model species as they possess sophisticated acoustic communications, but utilize a very different encoding system that evolved in an aquatic rather than terrestrial medium. Sperm whales, in particular, with their highly-developed neuroanatomical features, cognitive abilities, social structures, and discrete click-based encoding make for an excellent starting point for advanced machine learning tools that can be applied to other animals in the future. This paper details a roadmap toward this goal based on currently existing technology and multidisciplinary scientific community effort. We outline the key elements required for the collection and processing of massive bioacoustic data of sperm whales, detecting their basic communication units and language-like higher-level structures, and validating these models through interactive playback experiments. The technological capabilities developed by such an undertaking are likely to yield cross-applications and advancements in broader communities investigating non-human communication and animal behavioral research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
