Teaching a Massive Open Online Course on Natural Language Processing

This paper presents a new Massive Open Online Course on Natural Language Processing, targeted at non-English speaking students. The course lasts 12 weeks; every week consists of lectures, practical sessions, and quiz assignments. Three weeks out of 12 are followed by Kaggle-style coding assignments. Our course intends to serve multiple purposes: (i) familiarize students with the core concepts and methods in NLP, such as language modeling or word or sentence representations, (ii) show that recent advances, including pre-trained Transformer-based models, are built upon these concepts; (iii) introduce architectures for most demanded real-life applications, (iv) develop practical skills to process texts in multiple languages. The course was prepared and recorded during 2020, launched by the end of the year, and in early 2021 has received positive feedback.

A Joint Approach to Compound Splitting and Idiomatic Compound Detection

Applications such as machine translation, speech recognition, and information retrieval require efficient handling of noun compounds as they are one of the possible sources for out-of-vocabulary (OOV) words. In-depth processing of noun compounds requires not only splitting them into smaller components (or even roots) but also the identification of instances that should remain unsplitted as they are of idiomatic nature. We develop a two-fold deep learning-based approach of noun compound splitting and idiomatic compound detection for the German language that we train using a newly collected corpus of annotated German compounds. Our neural noun compound splitter operates on a sub-word level and outperforms the current state of the art by about 5%.