Langtech – Frequently Asked Questions

What is language technology?

Also known as LangTech, language technology involves using technology to comprehend, generate, and process human language. It includes using natural language processing (NLP), speech recognition, machine translation, sentiment analysis, etc.

What is covered by LangTech Services?

As one of the leading LangTech companies, Fidel offers various LangTech services, including localization engineering, software localization, website localization, game localization, transcription and annotation, chatbot data creation, and AI engine data creation. The company works as a comprehensive langtech partner to help companies optimize the localization route.

In which languages does Fidel provide langtech services?

Fidel provides langtech services in 100+ Indian and foreign languages. The latter includes Japanese, Chinese, Thai, Korean, Mongolian, Nepali, Burmese, Khmer, Malay, Russian, etc. In addition, some European languages include Polish, Spanish, Italian, German, French, Portuguese, Latvian, Turkish, Bulgarian, and many more. Middle Eastern languages include Kurdish, Arabic, Urdu, Dari, Uzbek, Hebrew, Indian languages (Hindi, Marathi, Gujarati, Malayalam, Tamil, Telugu, Kannada, Punjabi, Odiya, Assamese, Tulu, Goanese, Konkani, Marwadi, Maithili, Awadhi, etc.), etc.

Which ancillary langtech services does Fidel offer?

Ancillary services include bilingual staffing and consulting, interpretation services, multilingual DTP services, Japanese language training, and linguistic testing services.

Where is language technology used?

Language technology enjoys widespread use across virtual assistants, sentiment analysis tools, and language translation applications. It is also used in various applications like language translation apps, etc., for social media monitoring and automatic summarization systems.

How does machine translation (MT) work?

MT uses algorithms and models that help with the automated translation of text or speech. Tools with MT technology can leverage statistical methods, rule-based approaches, or modern neural networks to perform various translation-related tasks and activities.

Can language technology understand context and emotions in text or speech?

Yes. Advancements in NLP have resulted in systems that can comprehend context and emotions to some level. However, it must be noted that sentiment analysis and emotion recognition tools are constantly evolving in understanding and assessing emotions from text or speech data.

What kind of LangTech services does Fidel offer?

As a language technology expert, Fidel offers a range of langtech services, including localization engineering, localization, translation services, interpretation services, multilingual DTP services, Japanese language training, L1-L2 support, and multilingual testing and maintenance. Language technology services, in particular, include LLM development for AI-ML, L10N engineering, transcription and annotation, chatbot data creation, and AI engine data creation.

Are MTs accurate? If yes, how much?

MT accuracy varies with the context, language pair involved, and content complexities. Although contemporary neural network-based systems have improved their accuracy significantly, they may still confront challenges with a specific linguistic context like idioms, and highly localized phrases or words.

Is language technology important for businesses?

Yes, of course, it is. Businesses can leverage the capabilities of language technology for multilingual customer support, market research sentiment analysis, automatic translation of documents, and delivering highly personalized and seamless user experiences via various AI-driven multilingual chatbots or virtual assistants.

What are privacy concerns associated with language technology?

Some concerns include collecting and using personal data, particularly in applications involving voice assistants or chatbots.

Are there challenges involved in using language technology?

Yes. No technology is devoid of challenges. Language technology also isn’t an exception to this universal principle. Challenges in language technology include cultural variations, human linguistic ambiguity, regional differences, unique meanings, contextual differences, etc. While these challenges remain constant, low-resource language proves another set of challenges. Since they do not have many people speaking to them or using them as much, building an accurate MT system for them can prove challenging.

How can language technology help in language learning?

It can do so through various apps, online platforms, and tools providing interactive lessons, enabling vocabulary building, offering pronunciation feedback, and designing language exercises to help an individual’s language proficiency.

Are there any ethical considerations in language technology?

Yes. They include privacy, transparency in AI decision-making, fairness in algorithmic outcomes, data security, and reducing biases in AI systems to the best possible extent.

What role can language technology play in content summarization?

It can assist in summarizing large text volumes via algorithms to identify important information and generate concise summaries. Hence, it is useful in applications, including research papers, content curation, and news aggregation.

Can language technology be used in the legal realm?

Language technology can aid legal settings by summarizing legal texts, providing language translation services for international cases, automating document analysis, and helping with virtual discoveries via text mining.

Is there a chance of biases in language technology?

Yes. There is absolutely a chance of biases in language technology. It can be due to the data used to train models. These biases may result from cultural influences and prejudices or the quality and extent of historical data.

Is there a future for language technology? If yes, what is it?

Of course, language technology holds a promising future! How? Language technology will potentially involve advanced AI models to understand context, cultural nuances, and emotions in language better. Some projected developments could include enhanced MT, personalized language assistance, and conversational AI.