The transformer-based pre-trained language model BERT has helped to improve state-of-
the-art performance on many natural language processing (NLP) tasks. Using the same …

Pre-trained language models have been dominating the field of natural language
processing in recent years, and have led to significant performance gains for various …

Peeking into the inner workings of BERT has shown that its layers resemble the classical
NLP pipeline, with progressively more complex tasks being concentrated in later layers. To …

Readability research has a long and rich tradition, but there has been too little focus on
general readability prediction without targeting a specific audience or text genre. Moreover …

In this paper, we propose a new approach to construct a system of transformation rules for
the Part-of-Speech (POS) tagging task. Our approach is based on an incremental …

We present SICK-NL (read: signal), a dataset targeting Natural Language Inference in
Dutch. SICK-NL is obtained by translating the SICK dataset of Marelli et al.(2014) from …

Advances in computational language models increasingly enable adaptive support for self‐
regulated learning (SRL) in digital learning environments (DLEs; eg, via automated …

Whereas post-edited texts have been shown to be either of comparable quality to human
translations or better, one study shows that people still seem to prefer human-translated …

While human annotation is crucial for many natural language processing tasks, it is often
very expensive and time-consuming. Inspired by previous work on crowdsourcing, we …

This paper presents the LeTs Preprocess Toolkit, a suite of robust high-performance
preprocessing modules including Part-of-Speech Taggers, Lemmatizers and Named Entity …