  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.
