The effect of Artificial Intelligence (AI) on humans has increased over the last few years. From making our lives easier with online search recommendations, voice assistants and facial recognition logins, to facilitating advances in healthcare, identifying pandemics, and helping alleviate starvation. Artificial Intelligence is the ability of machines, especially computer systems, to simulate and perform human tasks and processes. Specific applications of AI include natural language processing, speech recognition, machine vision, and more.
In this blog, we will talk about AI and the benefits of using and implementing it in the localization industry.
Power of Artificial Intelligence
Artificial Intelligence offers several benefits making it an excellent tool that simulates human intelligence, including:
Reduce human errors: One of the advantages of AI is that it can significantly reduce errors and increase accuracy. The decisions taken by AI is decided according to information previously gathered and a certain set of algorithms. When programmed correctly, the errors can be reduced to zero.
No Risk: We can count on AI robots that are well programmed with a specific set of algorithms to perform risky tasks that humans cannot perform, such as defusing a bomb, diving into the deepest oceans and seas, flying to space, and much more.
Automation: AI can automate repetitive tasks without feeling fatigued, taking rests, or having breaks, as humans would need to.
Speed: AI systems can do tasks much faster than humans by finding and following specific patterns quickly. AI systems can analyze much larger datasets than humans and can perform complex mathematical calculations very quickly and accurately.
Artificial Intelligence and the Localization Industry
The localization industry is like many other industries that require human interactions and efforts. Artificial Intelligence can perform many localization-related tasks and processes faster, automatically, and with accuracy. Localization experts could utilize the use of AI in some areas in the industry such as Natural Language Processing (NLP), Machine Translation (MT), Terminology Mining, Speech to Text, and Text to Speech.
Natural Language Processing (NLP)
NLP is a subfield of AI that helps machines process and understand human language so that it can automatically perform repetitive tasks such as machine translation, spell check, text autocomplete, spam filters, etc.
One of the main reasons NLP is so important for businesses is that it can be used to analyze large volumes of text data, like customer support tickets, surveys, online reviews, news, social media comments, and much more. NLP can quickly help businesses analyze this data, provide analysis and reports, and enable decision-makers to make strategic decisions such as whether to localize a specific product, website, services manual, or marketing campaign for specific target markets.
Another usage of NLP in the localization industry is to analyze the source content to prepare a translation style guide and extract types of information for the linguists to process (i.e., person names, locations, organizations, etc.).
Machine Translation (MT)
Machine Translation is an automated translation performed by a well-educated computer installed with a machine translation engine. The MT engine provides text translations based on computer algorithms without human interaction. AI is used in the Machine Translation and Machine Learning subfields as the engine learns through high volume of source and target content. By using specific algorithms, the engine can provide the translations.
In the past, most MT products were based on algorithms that used statistical methods to try and provide the best possible translation for a given word. This technology is known as Statistical Machine Translation (SMT). SMT involves advanced statistical analysis to estimate the best possible translations for a word given the context of a few surrounding words. Recently, Neural Machine Translation (NMT) performs the process by attempting to model high-level abstractions into data, much closer to how it is undertaken by a human rather than the traditional statistical approach.
So, we can describe several types of Machine Translation as follows:
Rule-based machine translation (RBMT)
The earliest form of MT relies on linguistic rules and bilingual dictionaries for every language pair. A dictionary of the source language is used to select appropriate words in the target language. Syntax and grammar rules of both the source and target locale are observed, and the words taken from the dictionary are adapted appropriately (gender, grammar, word order, etc.).
Statistical Machine Translation (SMT)
In SMT, the MT engine uses statistical algorithms from analyzing existing human translations. It works by learning and comparing the source text with the model content. The translation is then generated based on the probability of occurrence in the target language. It works better for language pairs with similar word order.
Neural Machine Translation (NMT)
The NMT engine uses deep learning algorithms to train and educate itself and consistently improve. The engine functions similar to the human brain by using neural network models to create translation models.
Speech to text using AI is a field in computer science that enables computers to recognize spoken language and transcribe it into text. Speech-to-text is different from voice recognition as the software is trained to recognize and understand spoken words and write the spoken text in the same language. Voice recognition systems focus on recognizing the voice patterns of individuals and taking an action (writing the text) according to the voice pattern.
Speech-to-Text can be used in several fields such as:
- Customer Service: Many agencies rely on chatbots that are based on AI systems to help answer customers’ questions. Since many users prefer voice chat, accurate and efficient Speech-to-Text software can improve online customer support services.
- Smartphone personal assistants, (Siri in IOS, Amazon Alexa, and Google Assistant), use AI-based Speech-to-Text systems to recognize the user’s speech and convert it to text, like creating a note, composing a text message, or searching the internet.
- Electronic Documentation: Many fields require live transcription of speech to be used for reference later or to generate reports. Some of these fields are remote meetings, medical services, online lectures, classes, and much more.
AI and its multiple fields and subdomains are increasingly being used in various industries and businesses to help improve and speed up repetitive processes. As the localization industry has many repetitive processes that take time, using AI has become a major demand in the industry. We can rely on AI to automate many human tasks to be done by computers with accuracy.