Linguistic information is encoded at varying timescales (subwords, phrases,
etc.) and communicative levels, such as syntax and semantics. Contextualized
embeddings have analogously been found to capture these phenomena at
distinctive layers and frequencies. Leveraging these findings, we develop a
fully learnable frequency filter to identify spectral profiles for any given
task. It enables vastly more granular analyses than prior handcrafted filters,
and improves on efficiency. After demonstrating the informativeness of spectral
probing over manual filters in a monolingual setting, we investigate its
multilingual characteristics across seven diverse NLP tasks in six languages.
Our analyses identify distinctive spectral profiles which quantify cross-task
similarity in a linguistically intuitive manner, while remaining consistent
across languages-highlighting their potential as robust, lightweight task
descriptors.

通过开发可完全学习的频率过滤器，我们可以在不同层次和频率上捕捉语言信息。在单语言环境下，我们证明了频谱探测比手工滤波器具有更高的信息量，并在六种语言中的七个 NLP 任务中进行了多语言分析。这些分析鉴定了独特的频谱轮廓，是一种语言直观的方法，而且跨语言是一致的，并展示了其作为强大而轻量级任务描述符的潜力。