The evolution of language is inescapable, reflecting and driving significant societal changes and traditions. Language contact often drives innovation in how we speak, and with the influence of global cultures in the United States, a new narrative is unfolding in its linguistic tapestry.
In Southern Florida, for example, a rising tide of linguistic innovation has infused native inhabitants with a new lingo that has implications for the nature of data we use to teach our machines. The emergence of the “Miami Dialect” illustrates the power of language as a reflection of multicultural life and histories to create intricate and interconnected threads in Florida’s sunlit cityscape.
For technology to evolve to better suit our transforming lifestyles, so must the content and inputs that feed AI language models. Appen recognizes that in order to serve all users without bias, AI must adapt to regional dialects, as they play a pivotal role in fostering inclusivity.
The Linguistic Landscape: Understanding Dialects
Dialects are variants of a language that can differ in pronunciation, vocabulary, or grammar. Regionality, ethnicity or social groups can influence the type and frequency of variation within a language’s dialect. In the case of the Miami Dialect, it is primarily shaped by Spanish and English, reflecting the cultural heritage and history of the city. While there are a variety of Caribbean dialects at play in Miami, Cuban-Americans have played a significant role in shaping this new dialect. The language used by Cuban-Americans not only serves as a means of communication but also represents their unique identity and cultural heritage. The dominant innovation found in the Miami Dialect is the use of “calques,” direct translations of common Spanish phrases and idioms into English and are a reflection of the multiple waves of migration that can be traced to the Cuban exodus of the 1960s, interwoven with the fabric of the English spoken by Miamians today.
Linguistic Bridging for AI and Large Language Models
As we continue to rely on AI for everyday tasks, it becomes crucial for language models to reflect the diversity of human expression. Just as dialects evolve and adapt to societal changes, AI must also be equipped to understand and respond with a variety of linguistic nuances. Models trained solely on traditional forms of English, for example, may struggle to understand and communicate effectively with speakers of non-standard and emerging dialects. This diversity in language use alongside an ever-changing linguistic terrain presents a significant challenge for natural language processing (NLP) technologies, such as sentiment analysis, machine translation, and voice recognition. AI that cannot communicate or understand certain dialects doesn’t just limit people’s ability to leverage the technology but also runs the risk of further dividing cultures by erasing identities. As demonstrated in the Miami dialect, there are aspects of language structure that encode social identities of speakers.
By embracing linguistic diversity in AI, we can create more inclusive and comprehensive models that better reflect the eclectic world we live in. This also presents an opportunity for AI to become a bridge between different cultures and languages, promoting understanding and connection.
However, recognizing and incorporating dialects like the Miami Dialect, representing the unique culture and identity of its speakers, presents a host of challenges and opportunities for Large Language Models (LLMs) and Generative AI (Gen AI). How do we keep abreast of linguistic innovation in our development of language technologies?
For LLMs, the incorporation of this dialect equates to solving a puzzle with shifting pieces. The syntactic and semantic variations necessitate an adaptive approach, one that acknowledges and incorporates the dialect’s novel grammar and lexicon. Without updating, LLMs risk alienating a significant portion of English speakers, creating a rift where understanding should be resolute.
Similarly, Gen AI must evolve to not only understand but to articulate these dialects convincingly. This transition requires extensive modifications in AI models, equipping them with the necessary linguistic tools to accurately reflect regional language nuances. The implications are profound—adaptive AI can bridge cultural chasms and express solidarity with a diverse user base.
Societal and Business Implications of Emerging Dialects in AI
Beyond the linguistic implications within the dialect community, the business and societal ripples are of no small consequence. For businesses, embracing new forms of communication is a strategic necessity, offering a gateway to consumers in new markets or segments. Companies that integrate emerging dialects in their AI will not only connect better with local consumers but also demonstrate a commitment to diversity and inclusion in their branding.
From a societal viewpoint, the recognition and accommodation of new dialects on AI platforms signals belonging. Its inclusion validates the cultural significance of a language and acknowledges regional experiences as an integral component of the American story.
Propelling New Dialects into an AI-Driven World
What lies ahead for new dialects in an AI-driven world? It is highly plausible to anticipate a wider integration of regional English dialects into mainstream language models. As we continue to value cultural diversity, AI systems will adapt to represent a language mosaic that truly reflects our society, not just at a global or national level, but also at a regional and sub-regional level.
This adaptation goes beyond mere words and grammar. It is about amplifying identities and heritage through the language we share digitally. This holds true beyond the scope of Miami.
Reflecting on the remarkable efforts of the Linguistic Data Consortium for Indian Languages (LDC-IL) at the Central Institute of Indian Languages in Mysuru, it’s inspiring to witness how inclusive approaches can contribute to the evolution of AI and machine learning. Just as LDC-IL developed 16 new datasets encompassing multiple Indian languages such as Kannada, Tamil, Hindi, and Malayalam, everyone should strive to enrich linguistic models to encompass the full spectrum of human language.
These datasets have supported the development of technologies like Automatic Speech Recognition and Live Voice Translation in languages that possess unique phonetic and linguistic features due to their regional specificity. This underscores the necessity of including the Miami Dialect and other similar variations within our models and emphasizes the importance of disregarding ‘linguistic hierarchy’ in favor of authentic representation in AI.
To replicate such efforts, LLMs can employ a similar methodology: sourcing real-world data and expert verification to enhance understanding and generate output that embodies the richness of localized dialects, much like the specific nuances found in Indian English variations.
Building Bridges with Language Models: The Road Ahead for Appen
As a pioneer in language crowd-sourcing and high-quality AI training data, Appen stands at the nexus of this linguistic and technological convergence. We view our role as an indispensable component in sculpting and refining AI capabilities, celebrating and advocating incorporation of emerging dialects in new language models.
In our mission to elevate human insight as the cornerstone of effective AI solutions, Appen’s focus on linguistic inclusivity is by design. By recognizing new dialects as valuable assets, Appen paves the way for AI to resonate with the hearts and minds of consumers globally.
For Appen, the onus is twofold: to train AI models capable of understanding and responding in culturally relevant dialects while also fostering an environment free from bias that appreciates and respects linguistic diversity. Success hinges on our ability to blend unparalleled expertise with a flair for innovation, ensuring the AI of tomorrow embodies the spirit of the new English spoken today.
Embracing the Linguistic Mosaic: A Transformative Opportunity
The emergence of new dialects, like what we are seeing in Miami, is more than a linguistic novelty; it is a transformative continuum in the cultural journey of America. It beckons us to redefine our notion of “native” and “foreign” and elevates the need for humans to stay in the loop in the development of AI as we reimagine our own ways of communicating over time. It’s a narrative happening all over the world, all the time.
As we embrace this linguistic mosaic, we forge connections both artificial and deeply human. The Gen AI, which will converse in localized dialects, won’t merely be a marvel of technology but a testament to the inclusive, diverse society it is designed to serve.
The Faces and Voices of the AI Future
The Miami Dialect stands as one example of the adaptive spirit of language and the tapestry of experiences that shape it. As we look toward an AI-led future, we must imbue our language models with this same vitality and flexibility to ensure they resonate with the vast and varied human landscape they are intended to serve.
Appen’s narrative, rooted in linguistically empowered AI, is on the brink of a new chapter—one that celebrates the diversity and dynamism inherent to the human-technology interface. The company’s dedication to this vision not only affirms their role as shapers of the AI future but promises a society where the nuances of our diversity are not merely tolerated but celebrated and integrated into the very heartbeat of our technological advancements.
The language of AI has the potential to become a bridge, a meeting ground, a shared space where our rich diversity finds expression. In the case of the Miami Dialect, and similar linguistic phenomena across the globe, it is through understanding and adaptation that we can truly empower the aspirations of both AI and the people it serves.
