When applying automated speech recognition (ASR) for Belgian Dutch (Van Dyck et al. 2021), the output consists of an unsegmented stream of words, without any punctuation. A next step is to perform segmentation and insert punctuation, making the ASR output more readable and easy to manually correct. As far as we know there is no publicly available punctuation insertion system for Dutch that functions at a usable level. The model we present here is an extension of the models of Guhr et al. (2021) for Dutch and is made publicly available. We trained a sequence classification model, based on the Dutch language model RobBERT (Delobelle et al. 2020). For every word in the input sequence, the models predicts a punctuation marker that follows the word. We have also extended a multilingual model, for cases where the language is unknown or where code switching applies. When performing the task of segmentation, the application of the best models onto out of domain test data, a sliding window of 200 words of the ASR output stream is sent to the classifier, and segmentation is applied when the system predicts a segmenting punctuation sign with a ratio above threshold. Results show to be much better than a machine translation baseline approach.

Abbreviations present a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors. They can also make the text less readable, especially in reference printed books, where they are extensively used. Abbreviations are especially problematic in low-resource settings, where systems are less robust to begin with. In this paper, we propose a new method for addressing the problems caused by a high density of domain-specific abbreviations in a text. We apply this method to the case of a Slovenian biographical lexicon and evaluate it on a newly developed gold-standard dataset of 51 Slovenian biographies. Our abbreviation identification method performs significantly better than commonly used ad-hoc solutions, especially at identifying unseen abbreviations. We also propose and present the results of a method for expanding the identified abbreviations in context.

Will self-driving cars be snitches? Are you familiar with the expression that someone is a fink or a no-good dirty rat? Perhaps you might be more acquainted with other ways that this is commonly depicted such as those that are characterized as a weasel, a snitch, or a stoolie. Let's add to the matter a vexing ethical question, namely whether someone can be considered a stool pigeon or a squealer even if they are reporting on something that was an illegal or unlawful act? You would normally be tempted to assert that reporting a prohibited act is entirely appropriate and the tipster or whistleblower ought to be rewarded rather than ostracized as a tattler or snitch. Okay, consider a real-world example and see how you do. You are driving along on your daily journey to the office. There is a stop sign at an upcoming intersection.

BERT is now the go-to model framework for NLP tasks in industry, in about a year after it was published by Google AI. When released, it achieved state-of-the-art results on a variety of NLP benchmarks. It's referred to as a framework because BERT is not a model per se, but in the words of the authors themselves, it is a "method of pre-training language representations, meaning that we train a general-purpose "language understanding" model on a large text corpus (like Wikipedia), and then use that model for downstream NLP tasks that we care about (like question answering)." For the purpose of this blogpost, when we refer to a BERT model, we mean a model based on the BERT architecture and fine tuned for a particular task using pre-trained weights. Several papers have attempted to explain it, and created a field that the people at HuggingFace call Bertology .

Pedants, imagine how much more relaxed your life would be if artificial intelligence automatically corrected grammar mistake's in online forum and social network posts. Never again would you explode with frustration and anger over misplaced apostrophe's, commas, full stop's and exclamation! The faults could be fixed up by machine-learning software, and your soul would be soothed. Yes, software of the kind built by Mengyi Shan, a mathematics student at Harvey Mudd College in California, USA. She trained recurrent neural networks to restore missing punctuation in text.