Ask ChatGPT – October 2, 2023

My Conversations with ChatGPT
Machine Translation (MT) and translations generated by AI have brought about a paradigm shift in the translation industry. The output of MT will continue to improve, especially when associated with clients’ translation memories and human editing. Language Service Providers are adapting and are including MT in their processes, when MT is applicable. While linguists will continue to be essential in the process, their role will shift from that of translators to validators of content generated by MT. At Excel Translations, we wanted to know what AI thinks about all of this and decided to have an open conversation on the matter with ChatGPT. I will regularly publish a question and ChatGPT’s response, uncensored.

QUESTION 63:

How often are machine translation tools updated?

Chat GPT’s Response:

The frequency of updates for machine translation tools can vary widely depending on the specific tool, the organization or company behind it, and the goals of the updates. Here are some factors that can influence the update frequency:

1. Commercial Machine Translation Services: Commercial machine translation services like Google Translate, Microsoft Translator, and DeepL often receive regular updates. These updates may include improvements to the translation models, the addition of new languages, and enhancements to the user interface. These services typically aim to stay competitive and provide the best possible translations to their users, so they may release updates several times a year or even more frequently.
2. Open Source Machine Translation Models: Open source machine translation models like OpenNMT, Marian-NMT, and Fairseq are developed by the open-source community and can have varying update frequencies. Updates may depend on the availability of new research, contributions from the community, and the project’s development roadmap. Some open source models may receive frequent updates, while others may have longer release cycles.
3. Research-Based Models: Research institutions and companies involved in natural language processing (NLP) and machine translation often release new models and research papers. These models are sometimes updated as new research findings emerge. The update frequency can depend on the pace of research progress, and it may range from a few times a year to less frequent updates.
4. Custom Models: Organizations that develop custom machine translation models for their specific needs may update them as required. The update frequency will depend on factors such as changes in data, domain-specific needs, and available resources.
5. Data Availability: Machine translation models rely heavily on large datasets, and the availability of new and improved datasets can drive updates. If more high-quality training data becomes available for a particular language pair or domain, it may prompt an update to improve translation quality.
6. Technology Advancements: Advances in NLP and machine learning techniques can lead to updates in machine translation models. For example, the development of more efficient model architectures or training methods may trigger updates.

In summary, the update frequency for machine translation tools varies based on factors such as the type of tool, the organization or community supporting it, research advancements, data availability, and user demands. Some tools receive frequent updates to improve translation quality and add new features, while others may have less frequent updates based on their specific use cases and requirements. Users interested in the latest updates for a particular machine translation tool should refer to official announcements from the tool’s developers or organizations.