What Is Machine Learning? Definition, Types, and Examples

Explore the benefits of generative AI and ML and learn how to confidently incorporate these technologies into your business. Deep learning requires a great deal of computing power, which raises concerns about its economic and environmental sustainability. A full-time MBA program for mid-career leaders eager to dedicate one year of discovery for a lifetime of impact. A doctoral program that produces outstanding scholars who are leading in their fields of research. According to AIXI theory, a connection more directly explained in Hutter Prize, the best possible compression of x is the smallest possible software that generates x.

Learn more about this exciting technology, how it works, and the major types powering the services and applications we rely on every day. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view. In general, the effectiveness and the efficiency of a machine learning solution depend on the nature and characteristics of data and the performance of the learning algorithms. Besides, deep learning originated from the artificial neural network that can be used to intelligently analyze data, which is known as part of a wider family of machine learning approaches [96]. Thus, selecting a proper learning algorithm that is suitable for the target application in a particular domain is challenging. The reason is that the purpose of different learning algorithms is different, even the outcome of different learning algorithms in a similar category may vary depending on the data characteristics [106].

A dataset is a dictionary-like object that holds all the data and some

metadata about the data. This data is stored in the .data member,

which is a n_samples, n_features array. In the case of supervised

problems, one or more response variables are stored in the .target member. In general, a learning problem considers a set of n

samples of

data and then tries to predict properties of unknown data.

In this paper, we have conducted a comprehensive overview of machine learning algorithms for intelligent data analysis and applications. According to our goal, we have briefly discussed how various types of machine learning methods can be used for making solutions to various real-world issues. A successful machine learning model depends on both the data and the performance of the learning algorithms. The sophisticated learning algorithms then need to be trained through the collected real-world data and knowledge related to the target application before the system can assist with intelligent decision-making. We also discussed several popular application areas based on machine learning techniques to highlight their applicability in various real-world issues. Finally, we have summarized and discussed the challenges faced and the potential research opportunities and future directions in the area.

Machine learning certifications can help you stand out from other candidates for data science and programming jobs. Whether you complete a course or pass an exam, certificates represent accomplishment. They can help you Chat GPT demonstrate your knowledge, experience, and credibility in machine learning. In the following article, you can compare five popular machine learning certifications and learn how to choose one that’s right for you.

The performance of algorithms typically improves when they train on labeled data sets. This type of machine learning strikes a balance between the superior performance of supervised learning and the efficiency of unsupervised learning. In machine learning and data science, high-dimensional data processing is a challenging task for both researchers and application developers. Thus, dimensionality reduction which is an unsupervised learning technique, is important because it leads to better human interpretations, lower computational costs, and avoids overfitting and redundancy by simplifying models. Both the process of feature selection and feature extraction can be used for dimensionality reduction.

What is the Best Programming Language for Machine Learning?

Machine learning’s ability to extract patterns and insights from vast data sets has become a competitive differentiator in fields ranging from finance and retail to healthcare and scientific discovery. Many of today’s leading companies, including Facebook, Google and Uber, make machine learning a central part of their operations. Recommendation engines, for example, are used by e-commerce, social media and news organizations to suggest content based on a customer’s past behavior. Machine learning algorithms and machine vision are a critical component of self-driving cars, helping them navigate the roads safely. In healthcare, machine learning is used to diagnose and suggest treatment plans.

This enables the machine learning algorithm to continually learn on its own and produce the optimal answer, gradually increasing in accuracy over time. Two of the most widely adopted machine learning methods are supervised learning and unsupervised learning – but there are also other methods of machine learning. Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations. Among the association rule learning techniques discussed above, Apriori [8] is the most widely used algorithm for discovering association rules from a given dataset [133]. The main strength of the association learning technique is its comprehensiveness, as it generates all associations that satisfy the user-specified constraints, such as minimum support and confidence value.

Our results directed us to focus on the second approach, which offers several advantages. First, changing the threshold for one language did not affect the performance of the other (which is not true in the first setting). Second, this approach generalizes better to out-of-domain data, which is our primary use case (Wikipedia → web data). Finally, a single classifier has the added benefit of being computationally simpler, thus streamlining the language identification process. (A previous detector quality analysis showed that a higher precision was reached in this situation). We added this toxicity filtering procedure as an option to the filtering process and experimented with or without it for comparison.

Example of Machine Learning

Other advancements involve learning systems for automated robotics, self-flying drones, and the promise of industrialized self-driving cars. Machine learning is a form of artificial intelligence (AI) that can adapt to a wide range of inputs, including large data sets and human instruction. The algorithms also adapt in response to new data and experiences to improve over time. In addition to these most common deep learning methods discussed above, several other deep learning approaches [96] exist in the area for various purposes.

Choosing the right algorithm for a task calls for a strong grasp of mathematics and statistics. Training machine learning algorithms often involves large amounts of good quality data to produce accurate results. The results themselves can be difficult to understand — particularly the outcomes produced by complex algorithms, such as the deep learning neural networks patterned after the human brain. Deep learning is part of a wider family of artificial neural networks (ANN)-based machine learning approaches with representation learning.

Machine Learning Tasks and Algorithms

Keep in mind however that not all scikit-learn estimators attempt to

work in float32 mode. For instance, some transformers will always

cast their input to float64 and return float64 transformed

values as a result. Scikit-learn estimators follow certain rules to make their behavior more

predictive. These are described in more detail in the Glossary of Common Terms and API Elements. Operationalize AI across your business to deliver benefits quickly and ethically. Our rich portfolio of business-grade AI products and analytics solutions are designed to reduce the hurdles of AI adoption and establish the right data foundation while optimizing for outcomes and responsible use.

Deep learning models can be distinguished from other neural networks because deep learning models employ more than one hidden layer between the input and the output. This enables deep learning models to be sophisticated in the speed and capability of their predictions. Random forest models are capable of classifying data using a variety of decision tree models all at once. Like decision trees, random forests can be used to determine the classification of categorical variables or the regression of continuous variables. These random forest models generate a number of decision trees as specified by the user, forming what is known as an ensemble. Each tree then makes its own prediction based on some input data, and the random forest machine learning algorithm then makes a prediction by combining the predictions of each decision tree in the ensemble.

Thus, to build effective models in various application areas different types of machine learning techniques can play a significant role according to their learning capabilities, depending on the nature of the data discussed earlier, and the target outcome. In Table 1, we summarize various types of machine learning techniques with examples. In the following, we provide a comprehensive view of machine learning algorithms that can be applied to enhance the intelligence and capabilities of a data-driven application. Thus, the key contribution of this study is explaining the principles and potentiality of different machine learning techniques, and their applicability in various real-world application areas mentioned earlier.

Students learn how to apply powerful machine learning techniques to new problems, run evaluations and interpret results, and think about scaling up from thousands of data points to billions. In the field of NLP, improved algorithms and infrastructure will give rise to more fluent conversational AI, more versatile ML models capable of adapting to new tasks and customized language models fine-tuned to business needs. The work here encompasses confusion matrix calculations, business key performance indicators, machine learning metrics, model quality measurements and determining whether the model can meet business goals. The goal is to convert the group’s knowledge of the business problem and project objectives into a suitable problem definition for machine learning. Questions should include why the project requires machine learning, what type of algorithm is the best fit for the problem, whether there are requirements for transparency and bias reduction, and what the expected inputs and outputs are. As the volume of data generated by modern societies continues to proliferate, machine learning will likely become even more vital to humans and essential to machine intelligence itself.

Contributed to the data workstream of the project, which includes developing tools to facilitate data mining, cleaning and consolidation. Implemented automatic and human evaluations of NLLB, including but not limited to quality, bias and toxicity. Provided crucial technical and organizational leadership to help materialize this overall project.

Machine learning is a field within artificial intelligence and so the two terms cannot be used interchangeably. How machine learning works can be better explained by an illustration in the financial world. Traditionally, investment players in the securities market like financial researchers, analysts, asset managers, and individual investors scour through a lot of information from different companies around the world to make profitable investment decisions.

The creators of AlphaGo began by introducing the program to several games of Go to teach it the mechanics. Then it began playing against different versions of itself thousands of times, learning from its mistakes after each game. AlphaGo became so good that the best human players in the world are known to study its inventive moves. Whether you’re looking to become a data scientist or simply want to deepen your understanding of the field of machine learning, enrolling in an online course can help you advance your career. Enterprise machine learning gives businesses important insights into customer loyalty and behavior, as well as the competitive business environment.

In the following, we summarize and discuss ten popular application areas of machine learning technology. Amid the enthusiasm, companies will face many of the same challenges presented by previous cutting-edge, fast-evolving technologies. New challenges include adapting legacy infrastructure to machine learning systems, mitigating ML bias and figuring out how to best use these awesome new powers of AI to generate profits for enterprises, in spite of the costs. Developing the right machine learning model to solve a problem can be complex.

Today, neural machine translation (NMT) systems can leverage highly multilingual capacities and even perform zero-shot translation, delivering promising results in terms of language coverage and quality. However, scaling quality NMT requires large volumes of parallel bilingual data, which are not equally available for the 7,000+ languages in the world1. Focusing on improving the translation qualities of a relatively small group of high-resource languages comes at the expense of directing research attention to low-resource languages, exacerbating digital inequities in the long run. To break this pattern, here we introduce No Language Left Behind—a single massively multilingual model that leverages transfer learning across languages. We developed a conditional computational model based on the Sparsely Gated Mixture of Experts architecture2,3,4,5,6,7, which we trained on data obtained with new mining techniques tailored for low-resource languages. Furthermore, we devised multiple architectural and training improvements to counteract overfitting while training on thousands of tasks.

Business requirements, technology capabilities and real-world data change in unexpected ways, potentially giving rise to new demands and requirements. Read about how an AI pioneer thinks companies can use machine learning to transform. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. In DeepLearning.AI and Stanford’s Machine Learning Specialization, you’ll master fundamental AI concepts and develop practical machine learning skills in the beginner-friendly, three-course program by AI visionary Andrew Ng.

This program gives you in-depth and practical knowledge on the use of machine learning in real world cases. Further, you will learn the basics you need to succeed in a machine learning career like statistics, Python, and data science. Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, affordable data storage. Usually, the availability of data is considered as the key to construct a machine learning model or data-driven real-world systems [103, 105].

• The current techniques used for training translation models are difficult to extend to low-resource settings, in which aligned bilingual textual data (or bitext data) are relatively scarce22.
• The parameters built alongside the model extracts only data about mining companies, regulatory policies on the exploration sector, and political events in select countries from the data set.
• Representing a complex example by a simple cluster ID makes clustering powerful.
• The autoencoder (AE) [15] is another learning technique that is widely used for dimensionality reduction as well and feature extraction in unsupervised learning tasks.
• To understand how MoE models are helpful for multilingual machine translation, we visualize similarities of experts in the MoE layers using heat maps (Fig. 1a–d).

For example, an algorithm may be fed images of flowers that include tags for each flower type so that it will be able to identify the flower better again when fed a new photograph. Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce a considerable improvement in learning accuracy. Many automatic translation quality assessment metrics exist, including model-based ones such as COMET65 and BLEURT66.

IBM watsonx is a portfolio of business-ready tools, applications and solutions, designed to reduce the costs and hurdles of AI adoption while optimizing outcomes and responsible use of AI. The system used reinforcement learning to learn when to attempt an answer (or question, as it were), which square to select on the board, and how much to wager—especially on daily doubles. Watch a discussion with two AI experts about machine learning strides and limitations. Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.

Approximately 70 percent of machine learning is supervised learning, while unsupervised learning accounts for anywhere from 10 to 20 percent. Machine learning is an exciting branch of Artificial Intelligence, and it’s all around us. Machine learning brings out the power of data in new ways, such as Facebook suggesting articles in your feed.

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature

Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for

future research directions and describes possible research applications. Machines that possess a “theory of mind” represent an early form of artificial general intelligence. In addition to being able to create representations of the world, machines of this type would also have an understanding of other entities that exist within the world. Regardless of how far we are from achieving AGI, you can assume that when someone uses the term artificial general intelligence, they’re referring to the kind of sentient computer programs and machines that are commonly found in popular science fiction.

Machine learning methods

Through the course, you’ll also analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. As the algorithm is trained and directed by the hyperparameters, parameters begin to form in response to the training data. These parameters include the weights and biases formed by the algorithm as it is being trained. The final parameters for a machine learning model are called the model parameters, which ideally fit a data set without going over or under. Before machine learning engineers train a machine learning algorithm, they must first set the hyperparameters for the algorithm, which act as external guides that inform the decision process and direct how the algorithm will learn.

Over time, the algorithm would become modified by the data and become increasingly better at classifying animal images. For starters, machine learning is a core sub-area of Artificial Intelligence (AI). ML applications learn from experience (or to be accurate, data) like humans do without direct programming.

“Types of Real-World Data and Machine Learning Techniques”, which is increasing day-by-day. Extracting insights from these data can be used to build various intelligent applications in the relevant domains. For instance, to build a data-driven automated and intelligent cybersecurity system, the relevant cybersecurity data can be used [105]; to build personalized context-aware smart mobile applications, the relevant mobile data can be used [103], and so on. Thus, the data management tools and techniques having the capability of extracting insights or useful knowledge from the data in a timely and intelligent way is urgently needed, on which the real-world applications are based. The 2000s were marked by unsupervised learning becoming widespread, eventually leading to the advent of deep learning and the ubiquity of machine learning as a practice.

Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Decision trees are data structures with nodes that are used machine learning purpose to test against some input data. The input data is tested against the leaf nodes down the tree to attempt to produce the correct, desired output.

And beyond computation, which machines have long been faster at than we have, computers and other devices are now acquiring skills and perception that were once unique to humans and a few other species. But we tend to view the possibility of sentient machines with fascination as well as fear. Twentieth-century theoreticians, like computer scientist and mathematician Alan Turing, envisioned a future where machines could perform functions faster than humans. Personal calculators became widely available in the 1970s, and by 2016, the US census showed that 89 percent of American households had a computer.

Today, the method is used to construct models capable of identifying cancer growths in medical scans, detecting fraudulent transactions, and even helping people learn languages. But, as with any new society-transforming technology, there are also potential dangers to know about. As researchers attempt to build more advanced forms of artificial intelligence, they must also begin to formulate more nuanced understandings of what intelligence or even consciousness precisely mean.

Each of these machine learning algorithms can have numerous applications in a variety of educational and business settings. The basic concept of machine learning in data science involves using statistical learning and optimization methods that let computers analyze datasets and identify patterns (view a visual of machine learning via R2D3). Machine learning techniques leverage data mining to identify historic trends and inform future models. Machine learning also performs manual tasks that are beyond our ability to execute at scale — for example, processing the huge quantities of data generated today by digital devices.

We call one of those sets the training set, on which we

learn some properties; we call the other set the testing set, on which

we test the learned properties. Still, most organizations either directly or indirectly through ML-infused products are embracing machine learning. According to the “2023 AI and Machine Learning Research Report” from Rackspace Technology, 72% of companies surveyed said that AI and machine learning are part of their IT and business strategies, and 69% described AI/ML as the most important technology.

• In formal educational settings, for instance, students and educators belonging to low-resource language groups could, with the help of NLLB-200, tap into more books, research articles and archives than before.
• Many of the algorithms and techniques aren’t limited to just one of the primary ML types listed here.
• For example, an early layer might recognize something as being in a specific shape; building on this knowledge, a later layer might be able to identify the shape as a stop sign.
• However, some languages, such as Chinese or Thai, do not use spaces to separate words, and word segmentation tools may not be readily available.

Although LID could be seen as a solved problem in some domains24, it remains an open challenge for web data25,26. Specifically, issues coalesce around domain mismatch26, similar language disambiguation27 and successful massively multilingual scaling28. Leaders of these organizations consistently make larger investments in AI, level up their practices to scale faster, and hire and upskill the best AI talent. More specifically, they link AI strategy to business outcomes and “industrialize” AI operations by designing modular data architecture that can quickly accommodate new applications.

If we want to use a lot of data to train general robot policies, then we first need deployable robots to get all this data. In an effort to train better multipurpose robots, MIT researchers developed a technique to combine multiple sources of data across domains, modalities, and tasks using a type of generative AI known as diffusion models. For a machine or program to improve on its own without further input from human programmers, we need machine learning. At its most basic level, the field of artificial intelligence uses computer science and data to enable problem solving in machines. In this article, you’ll learn more about AI, machine learning, and deep learning, including how they’re related and how they differ from one another.

It has now been widely acknowledged that multilingual models have demonstrated promising performance improvement over bilingual models12. However, the question remains whether massively multilingual models can enable the representation of hundreds of languages without compromising quality. Our results demonstrate that doubling the number of supported languages in machine translation and maintaining output quality are not mutually exclusive endeavours. Our final model—which includes 200 languages and three times as many low-resource languages as high-resource ones—performs, as a mean, 44% better than the previous state-of-the-art systems. This paper presents some of the most important data-gathering, modelling and evaluation techniques used to achieve this goal. With simple AI, a programmer can tell a machine how to respond to various sets of instructions by hand-coding each “decision.” With machine learning models, computer scientists can “train” a machine by feeding it large amounts of data.

As for the precise meaning of “AI” itself, researchers don’t quite agree on how we would recognize “true” artificial general intelligence when it appears. There, Turing described a three-player game in which a human “interrogator” is asked to communicate via text with another human and a machine and judge who composed each response. If the interrogator cannot reliably identify the human, then Turing says the machine can be said to be https://chat.openai.com/ intelligent [1]. In this article, you’ll learn more about artificial intelligence, what it actually does, and different types of it. In the end, you’ll also learn about some of its benefits and dangers and explore flexible courses that can help you expand your knowledge of AI even further. Lists are based on professional translations from English, which were then heuristically adapted by linguists to better serve the target language.

We find that vanilla MoE models with overall dropout are suboptimal for low-resource languages and significantly overfit on low-resource pairs. To remedy this issue, we designed Expert Output Masking (EOM), a regularization strategy specific to MoE architectures, and compared it with existing regularization strategies, such as Gating Dropout40. We find that Gating Dropout performs better than vanilla MoE with overall dropout but is outperformed by EOM.

Reference 41 proposes spBLEU, a BLEU metric based on a standardized SentencePiece model (SPM) covering 101 languages, released alongside FLORES-101. In this work, we provide SPM-200 along with FLORES-200 to enable the measurement of spBLEU. Our best-performing model was trained with softmax loss over two epochs with a learning rate of 0.8 and embeddings with 256 dimensions.

If each sample is

more than a single number and, for instance, a multi-dimensional entry

(aka multivariate

data), it is said to have several attributes or features. Machine learning projects are typically driven by data scientists, who command high salaries. Actions include cleaning and labeling the data; replacing incorrect or missing data; enhancing and augmenting data; reducing noise and removing ambiguity; anonymizing personal data; and splitting the data into training, test and validation sets.

UC Berkeley (link resides outside ibm.com) breaks out the learning system of a machine learning algorithm into three main parts. Much of the technology behind self-driving cars is based on machine learning, deep learning in particular. In a 2018 paper, researchers from the MIT Initiative on the Digital Economy outlined a 21-question rubric to determine whether a task is suitable for machine learning.

At the same time, the program also introduces course takers to such specialized topics as time series analysis and survival analysis. We modelled multilingual NMT as a sequence-to-sequence task, in which we conditioned on an input sequence in the source language with an encoder and generated the output sequence in the expected target language with a decoder54. With the source sentence S, source language ℓs, and target language ℓt in hand, we trained to maximize the probability of the translation in the target language T—that is, P(T∣S, ℓs, ℓt). Below, we discuss details of the (1) tokenization of the text sequences in the source and target languages; and (2) model architecture with the input and output designed specifically for multilingual machine translation. For further details on the task setup, such as the amount of training data per language pair, please refer to Supplementary Information F or section 8 of ref. 34. In unsupervised learning, the training data is unknown and unlabeled – meaning that no one has looked at the data before.

As toxicity is culturally sensitive, attempting to find equivalents in a largely multilingual setting constitutes a challenge when starting from one source language. To address this issue, translators were allowed to forgo translating some of the source items and add more culturally relevant items. When building machine translation systems for thousands of different language pairs, a core question is which pairs reach certain levels of quality. Therefore, we needed meaningful scores that are comparable across language pairs.

Reinforcement learning (RL) is a machine learning technique that allows an agent to learn by trial and error in an interactive environment using input from its actions and experiences. Unlike supervised learning, which is based on given sample data or examples, the RL method is based on interacting with the environment. The problem to be solved in reinforcement learning (RL) is defined as a Markov Decision Process (MDP) [86], i.e., all about sequentially making decisions. An RL problem typically includes four elements such as Agent, Environment, Rewards, and Policy. In the Natural Language Processing with Deep Learning course, students learn how-to skills using cutting-edge distributed computation and machine learning systems such as Spark.

All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Linear regression is an algorithm used to analyze the relationship between independent input variables and at least one target variable. This kind of regression is used to predict continuous outcomes — variables that can take any numerical outcome. For example, given data on the neighborhood and property, can a model predict the sale value of a home?

Machine learning algorithms can use logistic regression models to determine categorical outcomes. When given a dataset, the logistic regression model can check any weights and biases and then use the given dependent categorical target variables to understand how to correctly categorize that dataset. Read on to learn about many different machine learning algorithms, as well as how they are applicable to the broader field of machine learning. Since a machine learning algorithm updates autonomously, the analytical accuracy improves with each run as it teaches itself from the data it analyzes.

Certificates typically emphasize training and academic accomplishment whereas certifications indicate professional experience or that you’ve passed a certification exam that requires specialized skills. In this proposed regularization strategy, we masked the expert output for a random fraction (peom) of the input tokens. For input tokens with dropped expert outputs, the first and/or second expert is effectively skipped. You can foun additiona information about ai customer service and artificial intelligence and NLP. 2, we masked both experts for the first token (x1 in red), chose not to mask any of the expert outputs for the second token (x2 in blue) and in the final scenario, masked only one expert for the last token (x3 in green). Overall, a sample of 55 language directions were evaluated, including 8 into English, 27 out of English, and 20 other direct language directions. The overall mean of calibrated XSTS scores was 4.26, with 38/55 directions scoring over 4.0 (that is, high quality) and 52/56 directions scoring over 3.0.

Independent variables and target variables can be input into a linear regression machine learning model, and the model will then map the coefficients of the best fit line to the data. In other words, the linear regression models attempt to map a straight line, or a linear relationship, through the dataset. Machine learning has played a progressively central role in human society since its beginnings in the mid-20th century, when AI pioneers like Walter Pitts, Warren McCulloch, Alan Turing and John von Neumann laid the groundwork for computation. The training of machines to learn from data and improve over time has enabled organizations to automate routine tasks that were previously done by humans — in principle, freeing us up for more creative and strategic work. Unsupervised machine learning is best applied to data that do not have structured or objective answer. Instead, the algorithm must understand the input and form the appropriate decision.

In the initial release of the Toxicity-200 lists, the average number of items in a toxicity detection list was 271 entries, whereas the median number of entries was 143. The latter may be a better measure of central tendency than the mean average, given that languages with a rich inflectional morphology constitute extreme outliers (for example, the Czech list had 2,534 entries and the Polish list 2,004). The chrF++ score38 overcomes the limitation of the BLEU score, which requires that a sentence can be broken up into word tokens.