Curriculum Learning (CL) aims to improve the outcome of model training by estimating the difficulty of training samples and scheduling them accordingly.In NLP, difficulty is commonly approximated using task-agnostic linguistic heuristics or human intuition, implicitly assuming that these signals correlate with what neural models find difficult to learn.We propose a four-quadrant categorisation of difficulty signalshuman vs. model and task-agnostic vs. task-dependent -and systematically analyse their interactions on a natural language understanding dataset.We find that task-agnostic features behave largely independently and that only task-dependent features align.These findings challenge common CL intuitions and highlight the need for lightweight, task-dependent difficulty estimators that better reflect model learning behaviour.

Leichte Sprache", the German counterpart to Simple English, is a regulated language aiming to facilitate complex written language that would otherwise stay inaccessible to different groups of people.We present a new sentencealigned monolingual corpus for Simple German -German.It contains multiple documentaligned sources which we have aligned using automatic sentence-alignment methods.We evaluate our alignments based on a manually labelled subset of aligned documents.The quality of our sentence alignments, as measured by the F1-score, surpasses previous work.