Leichte Sprache (Easy Language or Easy German) is a strongly simplified version of German geared toward a target group with limited language proficiency. In Germany, public bodies are required to provide information in Leichte Sprache. Unfortunately, Leichte Sprache rules are traditionally defined by non-linguists, they are not rooted in linguistic research and they do not provide precise decision criteria or devices for measuring the complexity of linguistic structures (Bock and Pappert,2023). For instance, one of the rules simply recommends the usage of simple rather than complex words. In this paper we, therefore, propose a model to determine word complexity. We train an XGBoost model for classifying word complexity by leveraging word-level linguistic and corpus-level distributional features, frequency information from an in-house Leichte Sprache corpus and human complexity annotations. We psycholinguistically validate our model by showing that it captures human word recognition times above and beyond traditional word-level predictors. Moreover, we discuss a number of practical applications of our classifier, such as the evaluation of AI-simplified text and detection of CEFR levels of words. To our knowledge, this is one of the first attempts to systematically quantify word complexity in the context of Leichte Sprache and to link it directly to real-time word processing.

Why do bilinguals switch languages within a sentence? The present observational study asks whether word surprisal and word entropy predict code-switching in bilingual written conversation. We describe and model a new dataset of Chinese-English text with 1476 clean code-switched sentences, translated back into Chinese. The model includes known control variables together with word surprisal and word entropy. We found that word surprisal, but not entropy, is a significant predictor that explains code-switching above and beyond other well-known predictors. We also found sentence length to be a significant predictor, which has been related to sentence complexity. We propose high cognitive effort as a reason for code-switching, as it leaves fewer resources for inhibition of the alternative language. We also corroborate previous findings, but this time using a computational model of surprisal, a new language pair, and doing so for written language.

We present an analysis of the internal mechanism of the recurrent neural model of sentence production presented by Calvillo et al. (2016). The results show clear patterns of computation related to each layer in the network allowing to infer an algorithmic account, where the semantics activates the semantically related words, then each word generated at each time step activates syntactic and semantic constraints on possible continuations, while the recurrence preserves information through time. We propose that such insights could generalize to other models with similar architecture, including some used in computational linguistics for language modeling, machine translation and image caption generation.