As AI technology expands into new markets, how effective is language AI at performing its job in new languages? For anyone following our series on language AI, the expansion of language functions and new opportunities for deployment of this technology in both foreign and domestic markets has clear and difficult challenges. As companies and researchers find new ways to leverage machine learning (ML) to grow conversational AI models, one obstacle is that the majority of these models are limited in scale and do not exist in complex languages or regional dialects. Although navigating this challenge and expanding the linguistic capability of language models requires investing heavily in ML, the integration and development more linguistically sophisticated technology could potentially open new doors for use and introduce prospective markets for growth.
While some technology today is capable of processing hundreds of languages, it is generally narrow in scale and limited to functions like language identification or translation. Despite this, growing interest in expanding the coverage of languages in AI for technology including chatbots, voice bots, and virtual assistants is making strides in linguistically diverse regions of the world where deploying these devices could be largely impactful. In Tunisia for instance, a tech startup has invested heavily in ML to train NLP language AI to be used in speech transcription services, automatic voice generation, chatbots, and voice-bot products, the goal of which is to expand on the technology’s ability to understand and process a range of Arabic dialects. To this extent, developing these models can help companies to bridge language barriers and more effectively communicate with customers, an important advantage in many regions of the world. As researchers in Qatar further pointed out, training models of this caliber is very much a complicated and expensive process, Furthermore, expanding the language capabilities for models in a range of languages has the benefit of being used in multiple settings, from which the groundwork for future technology with similar functions and could be developed.
Today, chatbots and similar language technology is becoming a more active way to automate and scale communications within industries of all kinds. Looking at the healthcare industry for instance, call centers are searching for ways to enhance efficiency and establish more effective modes of communication between patients and healthcare providers. Demand for these applications is widespread, and that extends to markets where there are multiple prevalent languages and dialects that may or may have well developed linguistic datasets or corresponding ML models. Regardless of the application for this cutting-edge technology, its rollout to specific sectors and markets demands adequate data that is both in-context and comprehensive of local communications culture, much in the way that well-localized content needs to adapt to the same criteria. Where in-country linguists with subject-matter expertise provide the vital link to ensuring individual documents are tailored to purpose and effective, large-scale, high-quality linguistic datasets are vital to the training of models that can replace human agents in communications like hotlines, customer service portals, and informational resources.
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