Machine translation (MT) is the process of automatically translating text from one language to another using computer software and without direct human involvement. In recent years, this field has changed significantly and has virtually revolutionized the way we think about translating natural languages. The main reason for this transformation has been the introduction of natural language processing translation technologies, which have improved machines' ability to understand and translate human language. But how did this technology become such a powerful tool? Let's try to figure it out in this article.
History of NLP
The history of NLP began in the mid-20th century, when scientists first began to think about how to make computers understand human language. The first experiments in machine translation in natural language processing appeared in the 1950s. At the same time, the famous Turing test was proposed, designed to test a machine's ability to think like a human. In the 1960s and 1970s, systems based on rules and dictionaries were created that analyzed sentences according to a predefined grammar.
One of the key figures who influenced the development of NLP was Noam Chomsky. In the mid-1950s, he developed the theory of generative grammar. Generative grammar was a universal scheme for describing language, independent of specific languages. This idea became the basis for a rationalistic approach in computational linguistics, in which language is described using universal rules that do not depend on a specific language. It gave researchers a methodological basis for developing the first models capable of analyzing sentence structure and syntactic relationships.
The first practical application of machine translation in NLP was in 1954, when IBM demonstrated a program for the IBM 701 that could translate text from Russian into English. In 1959, the Mark I Translating Device, the first automated translation system, was created for the U.S. Air Force. By the late 1960s, more advanced systems appeared, such as Terry Winograd’s SHRDLU, which could answer questions and take new facts into account. In the 1970s, William Woods developed the LUNAR system based on Augmented Transition Network (ATN) grammar, which modeled syntactic and semantic relationships in text.
In the 1980s and 1990s, strict rules were replaced by statistical methods. Systems began to learn from large text corpora, predicting probable translation and analysis options, which increased their accuracy and flexibility. The real breakthrough came in the 2010s with the advent of neural networks, transformers, and large language models (LLMs), which learned to take context into account, analyze entire phrases and paragraphs, generate responses, and translate texts almost like a human.
What Is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on how computers learn to recognize, analyze, and generate human speech. It is a very complex field where linguistics, mathematics, and artificial intelligence intersect. The ultimate goal is to make interactions between humans and machines more natural and understandable.
Thanks to NLP, voice assistants can carry out our commands, AI translators can translate phone calls, and chatbots can engage in meaningful dialogue. Advances in natural language processing have also made it possible to analyze emotions in text.
Using cutting-edge techniques, modern translation NLP systems can better understand the semantic and syntactic structure of the input text and generate more coherent and fluent translations.
The integration of natural language processing into machine translation systems has greatly expanded their capabilities, but despite the progress made in translation quality over the past decade, machine translation NLP systems still face a number of limitations.
Challenges in Natural Language Processing
The limitations that still need to be addressed in the field of natural language processing include the following:
- Low-resource Languages. For many of the world’s languages, there is too little available data, which makes training the NLP translation model difficult. To address this challenge, various approaches are used. One of them is transfer learning, where knowledge gained from high-resource languages is applied to low-resource ones. Another approach is data augmentation, which includes paraphrasing, synonym replacement, and rule-based text generation.
- Loss of Context. It is difficult for systems to distinguish between polysemous words, understand tone, idioms, or figurative meanings, and correctly link parts of a sentence. As a result, machine translation, text generation, and other NLP tasks often produce inaccurate or unnatural results. Modern models partially address these issues by taking sentence- and paragraph-level context into account. However, fully capturing all the nuances of human language remains impossible.
- Polysemy and Idioms. All languages contain polysemous words, proverbs, idioms, and fixed expressions that are difficult to translate automatically. This remains one of the most challenging problems in NLP, as models often misinterpret such words and expressions.
- Human-Level Quality. Modern neural machine translation in NLP systems still occasionally struggle to achieve consistently high translation quality, especially with complex and highly specialized texts. As a result, human intervention by professional linguists or translators is often required to improve output quality.
All of these aspects remind us that machine translation based on NLP is still an evolving field. Further research, innovation, and a multidisciplinary approach are necessary to address the complex challenges facing this domain.
NLP in Machine Translation
NLP helps machine translation models understand the structure, meaning, and context of a text before generating a translation. Modern systems are based on the Transformer neural architecture. The Transformer architecture is a neural model that analyzes all the words in a sentence and understands how they are related to each other. This model has replaced previous statistical and recurrent approaches, as it allows the context of each word to be taken into account immediately in the entire sentence, rather than processing the text one word at a time.
At the heart of transformers is the attention mechanism. It allows the model to determine which words in the source text are important at each moment of translation generation. For example, when translating a long sentence, the system can “focus” on the necessary parts, capturing the connections between words, even if they are far apart. This increases accuracy and makes the translation more natural.
The encoder–decoder architecture allows the process to be divided into two stages. First, the encoder mechanism creates contextual representations of the sentence — embeddings that reflect not only the meaning of each word, but also its role in the overall structure. Then the decoder uses these representations to build a word-by-word translation. In doing so, it takes into account the grammar, style, and characteristics of the target language.
Thanks to these architectural principles, translation systems are examples of NLP perceive language not as a set of individual words, but as a complex, interconnected structure of meanings. This is why modern translators can convey tone, idioms, and text style much more accurately than previous methods.
Key Stages of NLP
The NLP process consists of several key stages:
1. Data Collection
Building a high-quality data corpus is essential for machine translation in natural language processing. NLP models are trained on texts from diverse sources: articles, books, news websites, social media, forums, dialogues, domain-specific documents, and even speech transcriptions. The more diverse and extensive the corpus, the better the model can understand language structure, style, and the characteristics of different text types. For machine translation, parallel data — texts with identical content in two languages are additionally required.
2. Data Cleaning
After collection, the data undergoes mandatory filtering. This step removes unnecessary symbols, HTML tags, duplicate lines, “garbage” fragments, random letter sequences, emojis, misencoded segments, and other elements that could distort training. The goal is to retain only useful text that helps the model learn accurate language patterns.
3. Tokenization
Tokenization is the process of breaking text into minimal meaningful units: words, subwords, or sentences. Modern NLP systems often use subword tokenization (e.g., BPE, SentencePiece), which enables proper handling of rare words, complex forms, and new terms. Correct tokenization helps the model analyze sentence structure accurately and capture relationships between elements.
4. Case Normalization
To avoid confusion between words written in different cases, the text is standardized — typically converting all text to lowercase. This is especially important for languages where case does not affect meaning (e.g., English). Lowercasing reduces the number of unique tokens, simplifies model training, and lowers the likelihood of errors.
5. Stop-word Removal
Some words appear frequently but carry minimal semantic weight, such as prepositions, conjunctions, interjections, and articles. In certain tasks, like text classification or sentiment analysis, these words are removed to improve model performance. However, in modern machine translation systems, stop words are usually retained because they are important for correct syntactic structure.
6. Lemmatization / Stemming
At this stage, words are reduced to their base forms: for example, “goes,” “went,” “will go” all become “go”. This simplifies text analysis by reducing the variety of forms for the same lexeme. Lemmatization helps models better understand word meaning and grammatical function. In advanced models like Transformers, this step is partially replaced by contextual embeddings, but it remains important in traditional NLP pipelines.
7. Vectorization
Since machines cannot work directly with words, each word is converted into a numerical vector. Earlier methods included simple bag-of-words or one-hot encoding. Modern methods use embeddings — dense vectors that capture semantic relationships between words. For example, Word2Vec, GloVe, FastText, and later BERT and other Transformers create contextual representations where the same word can have different meanings depending on its surroundings.
8. Model Training
The final stage is training the chosen algorithm. Depending on the task, different methods are applied:
- Classical models: linear regression, Naive Bayes, SVM
- Neural networks: RNN, LSTM, GRU, previously used for sequences
- Modern transformers: architectures like BERT, GPT, mBERT, T5, which dominate today
During training, the model learns patterns, dependencies, and language structure, enabling it to perform tasks such as translation, text generation, sentiment analysis, and more.
Applications of NLP
NLP applications have become a part of our daily lives. They quietly help us search for information, translate texts, interact with virtual assistants, and even make our online shopping experiences more personalized. Here are some key areas where natural language processing technologies are used:
- Machine Translation. NLP technologies analyze the source text and generate a corresponding translation in the target language.
- Virtual Assistants. Virtual assistants like Siri and Alice use NLP for speech recognition and to maintain interactive conversations.
- Search Engines. Search engines such as Google and Yandex use NLP for query autocomplete and to provide the most relevant search results.
- Autocorrect and Text Prediction. When typing messages in any messenger, NLP suggests possible next words or automatically corrects misspelled words.
- Chatbots. NLP enables chatbots to communicate with users and resolve issues 24/7, significantly reducing the workload of customer support staff.
- Automated Content Moderation. Social media can be a toxic environment, but NLP helps automatically detect and remove offensive or inappropriate content.
- Text-to-Speech Tools. Text-to-speech systems use NLP to generate natural, expressive, and human-like speech from written text.
Lingvanex Is a Machine Translation Tool Using Natural Language Processing
One of the most notable achievements in natural language processing is the revival of powerful machine translation solutions. Lingvanex machine translation software is based on the latest advances in natural language processing, enabling it to provide users with high-quality translations of websites, phone calls, messages, and documents. The Lingvanex translation engine is powered by deep learning models trained on huge multilingual datasets. This allows the tool to take context into account, understand nuances, and produce translations into 109 languages that sound more natural and closer to human translation compared to traditional rule-based translation methods.
Conclusion
Machine translation has come a long way, transforming the way people from different countries communicate. The integration of neural networks and advanced natural language processing translation techniques has enabled accurate, contextual, and effective translations. Natural language processing (NLP) plays a key role in the evolution of machine translation, providing access to a variety of applications that greatly simplify people's daily lives.
However, there is still room for improvement, and ongoing research in this area promises further enhancements to machine translation technology, which will ultimately make global communication and collaboration more accessible.
