The shift of public debate to the digital sphere has been accompanied by a rise in online hate speech. While many promising approaches for hate speech classification have been proposed, studies often focus only on a single language, usually English, and do not address three key concerns: post-deployment performance, classifier maintenance and infrastructural limitations. In this paper, we introduce a new human-in-the-loop BERT-based hate speech classification pipeline and trace its development from initial data collection and annotation all the way to post-deployment. Our classifier, trained using data from our original corpus of over 422k examples, is specifically developed for the inherently multilingual setting of Switzerland and outperforms with its F1 score of 80.5 the currently best-performing BERT-based multilingual classifier by 5.8 F1 points in German and 3.6 F1 points in French. Our systematic evaluations over a 12-month period further highlight the vital importance of continuous, human-in-the-loop classifier maintenance to ensure robust hate speech classification post-deployment.

The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the Text Style Transfer (TST) task, which aims to change the stylistic properties of the text while retaining its style independent content. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. The field of TST research has burgeoned because of this symbiosis. This article aims to provide a comprehensive review of recent research efforts on text style transfer. More concretely, we create a taxonomy to organize the TST models and provide a comprehensive summary of the state of the art. We review the existing evaluation methodologies for TST tasks and conduct a large-scale reproducibility study where we experimentally benchmark 19 state-of-the-art TST algorithms on two publicly available datasets. Finally, we expand on current trends and provide new perspectives on the new and exciting developments in the TST field.

Interactive data exploration (IDE) is an effective way of comprehending big data, whose volume and complexity are beyond human abilities. The main goal of IDE is to discover user interest regions from a database through multi-rounds of user labelling. Existing IDEs adopt active-learning framework, where users iteratively discriminate or label the interestingness of selected tuples. The process of data exploration can be viewed as the process of training a classifier, which determines whether a database tuple is interesting to a user. An efficient exploration thus takes very few iterations of user labelling to reach the data region of interest. In this work, we consider the data exploration as the process of few-shot learning, where the classifier is learned with only a few training examples, or exploration iterations. To this end, we propose a learning-to-explore framework, based on meta-learning, which learns how to learn a classifier with automatically generated meta-tasks, so that the exploration process can be much shortened. Extensive experiments on real datasets show that our proposal outperforms existing explore-by-example solutions in terms of accuracy and efficiency.

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

Are you looking for Best Free Coursera Courses in 2023? You can earn a Coursera Certificate with Coursera free courses by applying for a Coursera scholarship and by doing Coursera paid courses. You are going to get a 7-day free trial on Coursera when you join and start your very first subscription to do Coursera Specializations for free. If you do not cancel your free trial you will be automatically transferred to paid subscription on the 8th Day. You can continue your Coursera Classes either by using Coursera App on mobile or any other device. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Learn and launch your career in Data Science with these best Coursera courses. A nine-course introduction to data science developed and taught by leading instructors. Develop programs to gather, clean, analyze, and visualize data. You will get new insights into your data. Learn to apply data science methods and techniques, and acquire analytical skills.

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Weight decay, also known as L2 regularization, is a technique used in machine learning to prevent overfitting by adding a penalty term to the objective function that is being optimized. The goal of weight decay is to reduce the complexity of the model by limiting the size of the weights, which can help to prevent overfitting and improve the generalization ability of the model. Weight decay is typically implemented by adding a term to the objective function that is proportional to the sum of the squares of the weights. The strength of the weight decay penalty is controlled by a hyperparameter called the decay rate or regularization strength, which determines the amount of weight decay applied to the model. For example, let's say we are training a linear regression model to predict the price of a house based on the number of bedrooms and the square footage.

'A confident bullshitter that can write very convincing nonsense': not a takedown of an annoying student or a former British prime minister, but a description of an artificial intelligence writing programme that is causing headaches for its makers. With fears in academia growing about a new AI chatbot that can write convincing essays – even if some facts it uses aren't strictly true – the Silicon Valley firm behind a chatbot released last month are racing to "fingerprint" its output to head off a wave of "AIgiarism" – or AI-assisted plagiarism. ChatGPT, an AI-based text generator that was released for public use in early December, has been praised and criticised alike for the quality of its output. Users can ask it questions ranging from simple factual queries ("What is the tallest mountain in Britain?") to absurd requests ("Write a limerick explaining the offside rule") and receive clear and coherent responses written in natural English. Headteachers and university lecturers have expressed concerns that ChatGPT, which can provide convincing human-sounding answers to exam questions, could spark a wave of cheating in homework and exam coursework.

You want success over the coming months and years? The number-one way to get there is through people -- building businesses through their creativity, passion, and full participation in decision-making. But right behind empowered people is the number-two vital ingredient for success: data. Data that can reveal to you what your customers want, how your business is running, and what's around the corner. Now, we have the key that unlocks the patterns that have long been hidden away in databases and applications.

In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.