Exploring Direct and Indirect Machine Translation
Introduction
In Chapter I we studied that natural language processing (NLP) is an area of artificial intelligence (AI) that:
Focuses on enabling computers to understand, interpret, and generate human language.
NLP involves the development of algorithms and models that can process and analyze natural language data, such as written text, speech, and even gestures.
NLP embraces a wide variety of applications, including:
Machine translation.
Sentiment analysis.
Chatbots and virtual assistants.
Text summary and language modeling.
In this chapter we will focus on machine translation.
Machine translation.
Machine translation in natural language processing (NLP) is the process of automatically translating text or speech from one language to another using computer algorithms:
It is a subfield of computational linguistics and artificial intelligence whose goal is to facilitate communication between people who speak different languages.
It consists of developing algorithms and models capable of analyzing and understanding input texts in one language and generating output texts in another.
It involves not only linguistic analysis and understanding, but also the ability to accurately capture the nuances and complexities of human language.
Machine translation (MT) can be classified according to a number of factors, such as:
MT by input type (text or speech).
MT by the direction of translation (for example, from English to French or from French to English).
MT by granularity (word, phrase, or sentence).
In Chapter III we studied ATs by type of text or voice input. Here we will continue with the other two classifications.
By the direction of the translation.
This refers to the direction in which a machine translation system translates a text from one language to another. There are two translation directions:
Direct translation: refers to the translation from a source language (English) to a target language (French). In other words, the machine translation system takes as input the text in the source language and produces as output the corresponding text in the target language.
Backward translation: This refers to the translation from a target language into a source language. In other words, the machine translation system takes the text in the target language as input and produces the corresponding text in the source language as output.
Translation direction algorithms:
Rules-based approach:
This approach is based on the idea that human language follows certain rules and patterns, and that these rules can be encoded to produce a translation.
Grammatical and linguistic rules are used to analyze the source text and generate a structured representation of it.
This can improve the quality of the translation compared to other approaches, as linguistic knowledge is incorporated directly into the system.
There are some that cannot always capture all the meaning and ambiguity of human language, which can limit the accuracy and quality of the translation.
The mochooss.wordpress.com website illustrates the steps of the algorithm:
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Statistical approach:
It uses statistical models to learn to translate text from one language to another.
The model learns from a training corpus containing aligned sentences in both languages.
It uses machine learning techniques to identify patterns and relationships between words in both languages.
It can handle the complexity and ambiguity of human language more effectively than the rule-based approach.
In addition, as the model learns from more data, its translation quality can improve over time.
The localizationlab.com website illustrates a diagram:
Neural network approach:
There are two main types of neural networks for machine translation:
Phrase-based models use a neural network to learn a vector representation of a complete sentence in one language, and then use this representation to generate a sentence in the other language.
Sequence-based models, on the other hand, use a neural network to predict the next word in the target sentence from the source sentence and previous words.
The attention model is based on the idea that not all words in a sentence are of equal importance in translation. Instead of treating all words equally, the attention model focuses on the keywords relevant to the translation and gives them more weight in the translation process.
Francisco Casacuberta Nolla, Álvaro Peris Abril, authors of the article Neural machine translation published in "Revista Tradumatica" illustrate a diagram of the functionality of this model:
Francisco Casacuberta Nolla, Álvaro Peris Abril (Revista Tradumatica)
Transformer model:
It is a deep learning model used in NLP machine translation. It was developed by Google in 2017 and has become one of the most popular and effective models for machine translation.
It is based on a neural network architecture that uses a technique called multiple attention to translate sentences from one language to another.
It uses several layers of interconnected neural networks, each of which focuses on various aspects of translation, such as the syntactic and semantic structure of sentences.
Multiple attention is a technique that allows the model to focus on various parts of the sentence during the translation process, thus improving the accuracy and consistency of the translation.
Jaime Sendra Berenguer in the article TRANSFORMER for Text Translation, published in médium.com, illustrates a diagram of the model:
GPT chat and direct and indirect machine translation
As a linguistic model, Chat GPT can help with machine translation for both direct and indirect translation. For direct translation, you can provide me with a sentence or text in one language and I can provide you with a translation in another language. For example, if you give me a sentence in Spanish, I can translate it into English.
As for indirect translation, I can also help you with tasks such as summarizing, paraphrasing, and simplifying a text in each language. This can be useful for situations where you need to communicate with someone who speaks a different language, or to understand a document written in a language you do not speak.
Big Data Applications:
Multilingual data processing: Machine translation can help process large volumes of multilingual data in real time, making it easier to extract insights and value from data.
Multilingual Customer Support: With machine translation, companies can offer multilingual customer support without the need to hire a large team of multilingual support agents.
Content localization: Machine translation can help localize content such as websites, marketing materials and product descriptions into multiple languages, making it easier to reach global audiences.
Multi-language search: Machine translation can be used to translate queries from one language to another, enabling multilingual searches and making it easier to find relevant information across language barriers.
Language learning: Machine translation can be used to provide language learners with real-time translations of texts and conversations, facilitating the learning of a new language.
Applications in the financial industry:
International Trade and Investment: Machine translation can help financial institutions and companies communicate and negotiate with international partners in their native language, facilitating global trade and investment.
Financial reports: With machine translation, financial reports can be translated into multiple languages, facilitating cross-border communication of financial information.
Compliance: Financial institutions can use machine translation to ensure compliance with regulations and policies in different regions and countries, even if they are written in different languages.
Customer Service: Machine translation can help financial institutions provide customer service in multiple languages, improving customer satisfaction and retention.
Fraud detection: Machine translation can be used to identify fraudulent activities in multiple languages and regions, helping financial institutions prevent and detect financial crime.
Applications in the real estate industry:
Multilingual Marketing: Machine translation can help real estate companies reach global audiences by translating marketing materials, such as property descriptions and brochures, into multiple languages.
Multilingual Customer Service: With machine translation, real estate companies can offer multilingual customer service, improving customer satisfaction and facilitating communication with customers who speak different languages.
International Real Estate Transactions: Machine translation can help real estate companies and buyers/sellers communicate and negotiate across language barriers in international real estate transactions, making it easier to conduct business and close deals.
Property Management: Machine translation can be used to translate leases, maintenance reports and other property management-related documents into multiple languages, facilitating communication with tenants and landlords who speak different languages.
Market Analysis: Machine translation can help real estate companies analyze real estate markets in different countries by translating news articles, market reports and other information sources into their native language, enabling better investment decisions and opportunities.
Closing remark:
Overall, machine translation can help different industry sectors expand their reach and operate more effectively in a global marketplace by breaking down language barriers and enabling communication and information exchange across regions and cultures.