The rapid evolution of artificial intelligence (AI) and machine learning technologies continues to redefine various aspects of our life, and language understanding is one of them. While AI has made significant strides in understanding and interpreting formal language, there is an essential facet it still finds challenging—the global language of the streets, “slang.” Slang is an organic and fluid aspect of language, continually transforming and challenging to structure into rigid rules that AI could easily understand. However, some AI strategies could help maneuver these challenges.
The first approach is incorporating lexical databases. Lexical databases comprise a considerable part of the solution to endowing AI with the ability to understand slang. They are repositories of words that include definitions, synonyms, antonyms, and other attributes. One widely recognized lexical database is WordNet, which not only has a rich ensemble of words but also relationships between words. However, WordNet is designed around proper language and does not contain slang, necessitating specialized slang databases.
Such databases are continually being developed. For instance, the Global Database of English Slang (GDoES), an open-source project, is a comprehensive and continually updated record of English slang. AI systems can leverage these resources to improve their understanding. Other resources like the Urban Dictionary provide an exhaustive compilation of slang, user-generated, keeping it updated and relevant. AI can track and learn these ever-evolving phrases and words using these databases.
Another strategy involves machine learning models specializing in understanding context. Context is a significant factor in understanding slang, which often depends on situational meanings. For instance, the phrase “spill the tea,” quite popular in internet culture, carries a different meaning when interpreted in a conventional sense. Machine learning models trained to discern context could better understand the intangible aspects of slang that a lexical database might not cover.
One way to accomplish this is through the application of neural networks, particularly Recurrent Neural Networks (RNNs). RNNs are used in tasks involving sequential data, like text. They can remember past information and connect it with current tasks, ideal for understanding context. RNN variants like Long Short Term Memory (LSTM) networks can further enhance context understanding by remembering or forgetting information based on future requirements.
In addition to the above strategies, other machine learning models like Bidirectional Encoder Representations from Transformers (BERT) models could help AI catch up with slang. BERT, developed by Google, is a transformer-based machine learning technique for Natural Language Processing (NLP) pre-training. This technology allows the system to understand the context of a word based on its surroundings, making it highly effective in understanding slang, idiomatic phrases, and other forms of colloquial language.
Crowdsourcing human insights is another promising strategy. Humans naturally understand and use slang, and this ability can be leveraged to create dynamic databases for AI training. For instance, users can be incentivized to provide explanations or context for certain slang words or phrases, similar to how the reCAPTCHA system is used to train AI for text and image recognition. This can create a symbiotic relationship where AI improves its grasp on slang through human users’ insights, who, in turn, enjoy an enhanced user experience as the AI becomes more intuitive.
Another strategy is cross-linguistic referencing. Languages often borrow slang terms and phrases from other languages, with English being a significant contributor. Bilingual AI models can use this to their advantage by identifying if a certain slang phrase in a language other than English has an equivalent English slang term or phrase. Such cross-linguistic understanding can enhance the AI’s language processing capabilities.
While these strategies can substantially equip AI to handle slang, it’s important to keep in mind that slang is ever-evolving, and it’s practically impossible for any AI to understand slang perfectly. However, a combination of several strategies would significantly augment AI’s understanding and interpretation of slang, making AI communication more engaging, relatable, and human-like.
On the downside, we also need to consider the ethical implications of AI understanding slang. While this might enhance communication, the misuse potential is also significant. For instance, hate speech, profanity, and cyberbullying often employ coded language or slang. AI understanding such usage may inadvertently enable harmful behaviors. Thus, it’s imperative to safeguard user interactions and strictly moderate AI usage.
AI’s understanding of slang is a frontier waiting to be explored thoroughly. A well-orchestrated symphony of lexical databases, machine learning models, crowdsourced human input, and cross-linguistic referencing could equip AI to navigate the world of slang efficiently. The result is a more intuitive, engaging, and human-like AI—for better or for worse, depending on how it’s implemented and regulated. As we move forward, AI that can handle slang will indeed be a game-changer, redefining the boundaries of communication and interaction in the digital landscape.
