Language independence plays a significant role in the machine-learning of natural language processing (NLP). Aside from being a type of scalability, it also adds various advantages to modern artificial intelligence (AI) technology.
This is made possible with appropriate data training of AI to multiple language translation by minimizing the complexity of the data collection and processes.
However, there are still a lot of things to consider before NLP can achieve language independence. This includes finding an adequate amount of raw data and doing extensive data annotation to make them more reliable for translating words and conversing with multilingual users.
In this article, we will discuss how it is possible to achieve language independence in NLP through data training that sources like translation companies can offer.
What is NLP?
Natural language processing or NLP is the ability of a computer to understand, manipulate and generate human language, thus creating an interaction between humans and computers. It is also a helpful tool that lessens the burden of repetitive tasks such as answering FAQs, translations, information retrieval, and more. It is also a great addition to make business intelligence, customer support, and global interactions more functional.
At first, NLP was deemed impossible by a lot of people until its development in the 1950s. But as years go by, its algorithms changed with more extensive data, deep learning, and statistical learning methods. Technological advancements bring forth the NLP to greater heights of machine learning, chatbots, and the internet of things (IoT). According to Semrush, about 86% of CEOs say that AI bots will become mainstream in their offices by 2021.
Here some of the benefits of NLP in different industries:
Data Analysis
NLP plays a significant role in business intelligence as it helps gather and analyze data efficiently. With smart visual encodings, it offers the best visualization based on the data semantics. It makes understanding and exploring data collections more convenient as it also provides keyword fragments to find the critical information right away. NLP can build a connection between analytics, developers, and other people who are not familiar with their jargon.
Smart Assistants
One of the best examples of using NLP is intelligent assistants. It includes Siri of Apple, Alexa of Amazon, and Google Assistant. It has voice recognition and advanced machine learning that enables it to interact with its users and follow voice instructions. Aside from phones, other gadgets and appliances are also starting to use them for light switches, security locks, thermostats, and more.
Text Analytics
To convert unstructured data to meaningful analytical data, developers use NLP with different machine and linguistic techniques. It helps scour customer messages, reviews, posts, and brand mentions to know their feedbacks. The analysis of customer interactions provides companies the ideas of what they should improve or retain to their products or services.
Language Translation
NLP is especially useful in linguistics to analyze and understand multiple languages with enough data training. It helps students, business people, translators, and clothes connect to the world and broaden their horizons. However, there are still some issues that most linguists see when it comes to this.
What Are the Linguistic Challenges in NLP?
NLP relies heavily on the data that it collects and analyzes, and if they cannot process the data correctly, it results in multiple issues in artificial intelligence. Even with the recent technological advancements, some cultural and linguistic factors that machine learning cannot comprehend still exist. For example, when directly translated, some words may mean different things to another language, which can cause misunderstandings.
Also, if the information is scarce, unbalanced, or too heterogeneous, it lessens the effectiveness of an NLP’s machine learning capabilities. On data-related issues, most of the causes would be the lack of resources for the desired language. There are a lot of data that you can get from popular languages such as English, Spanish, and Chinese. However, there are still thousands of languages around the world that are not given enough attention, and the data is scarce. Due to this, transferring data from a high resource language to a low resource one becomes challenging for NLP.
Moreover, if you have a massive amount of data, it would also be a tremendous challenge for your NLP to require them to scale up their current system. This is why it is difficult to find multilingual AIs covering over ten languages for human interaction.
Here are some of the linguistic issues that NLP encounters:
- They have a hard time understanding the semantic meanings of the words in multiple languages.
- They cannot differentiate the use of synonymous words in various sentences and expressions.
- Using co-reference to find the phrases related to a single entity due to extensive data
- Identifying irony and sarcasm and the irony of sentences and expressions are complex for them.
- Understanding colloquial words and slang in various places is complicated.
- They have less to no expertise in understanding technical techs containing industry jargon, such as in the medical and engineering fields.
- Finding an adequate amount of data training sets is challenging.
Due to these linguistic issues, data management professionals and AI developers still have a long way to go in creating the perfect NPL that can converse with humans efficiently. They also affect the quality and integrity of services that AI and IoT offer to its users.
How Data Training Works, and Why You Need a Translation Company
To answer complications and common issues in NLP regarding obtaining language independence, it is essential to implement deep learning and data training. This is possible by providing your AI with an adequate amount of raw data sets in different languages and training them.
What data training does is that it adds annotations to categorize the specific data. It could involve labeling people, brands, organizations, ages, and locations. This makes it easier for the AI to process, analyze, and appropriately react when interacting in human language. With proper linguistic text annotation, it is also possible to put labels in various nouns, verbs, adjectives, etc.
Depending on the language, the training data of NLP may differ due to varying character and grammatical rules. There is also a difference in the amount of available data to common and uncommon languages that people use. To ensure that there will be an adequate amount of data that data managers could utilize for their AI, it is ideal to have project managers, crowd workers, and translators.
Besides that, they can partner with a reliable translation company like Tomedes, Lionbridge, and Appen which offers training data sets for developers. It is more convenient to connect with them as they have millions of available information and documents in more than 150 languages worldwide. They can also assure that all the data translations are correct and reviewed by professional translators and linguists.
In addition, companies like Google offer extensive data sets that they have collected for years. Some international crowdsourcing companies such as Upwork and Clickworker are also reliable when looking for conversational text data in various languages.
However, it is essential to note that audios, videos, and text messages will not work as training data. They must first be organized and labeled into a training data set. Thus, coordinating with a translation company that offers annotations is still the ideal decision to build language independence in NLP through data training.
Key Takeaways
When yearning to build language independence for NLP, having enough labeled data training sets is essential. It measures the amount of information they can process and uses for human interactions and prevents linguistic issues. Thus, it is ideal to find reliable resources that can provide an adequate amount of annotated data in various languages, such as translation companies or raw data sets on Google and crowdsourcing websites.
