Learning a language, whether by humans or computers, involves understanding and producing linguistic forms, meanings, and functions within various contexts.
The process for computers, particularly in the realm of Natural Language Processing (NLP) and Machine Learning (ML), has notable parallels with human language learning and acquisition theories such as comprehensible input. Let's explore these similarities:
1. Exposure to Language Data
- Humans: Language acquisition in humans, especially in young children, occurs through extensive exposure to linguistic input within meaningful contexts. This exposure helps in developing an understanding of vocabulary, grammar, and usage norms.
- Computers: Similarly, computers "learn" languages by being exposed to vast amounts of text data. This data trains ML models to recognize patterns, structures, and semantics. The more diverse and comprehensive the dataset, the better the computer can understand and generate language.
2. Comprehensible Input
- Humans: Stephen Krashen's theory of "Comprehensible Input" (i+1) suggests that language acquisition occurs when learners are exposed to language slightly above their current level of competence, making it possible for them to understand the gist despite not knowing all words or structures.
- Computers: In machine learning, progressively challenging the model with complex inputs after mastering simpler patterns is akin to this. Models often start with basic language constructs before moving on to understand and generate more complex sentences and ideas.
3. Feedback Mechanism
- Humans: Feedback is crucial in human language learning. It helps learners correct errors, refine their understanding, and improve language proficiency over time.
- Computers: Computer models also rely on feedback, provided through error correction and adjustment algorithms during training (such as backpropagation in neural networks). This process adjusts the model's parameters to minimize errors in understanding or generating language.
4. Pattern Recognition and Generalization
- Humans: Humans learn languages by identifying patterns (in grammar, syntax, semantics) and applying these patterns to generate new utterances.
- Computers: ML models, especially those based on neural networks, excel at identifying patterns in data. They learn these patterns during training and can generalize from them to understand or produce new language content that they have not explicitly seen before.
5. Use of Context
- Humans: Context plays a significant role in how humans understand and produce language. It includes not just the linguistic context (words around a word) but also the situational, cultural, and emotional context.
- Computers: Advanced language models, like GPT (Generative Pre-trained Transformer), also consider context in processing language. They analyze the surrounding words and the broader context within the text to generate coherent and contextually appropriate responses.
6. Continuous Learning
- Humans: Language learning for humans is a lifelong process, involving continuous learning and adaptation as they are exposed to new vocabularies, expressions, and contexts.
- Computers: While ML models are trained in more discrete phases, the concept of continuous or lifelong learning is becoming increasingly important in AI, with efforts to develop models that can learn from new data or tasks without forgetting previously learned information.
The processes of language learning and acquisition, whether by humans or computers, share core principles of exposure to language, the importance of comprehensible input and feedback, pattern recognition and generalization, contextual understanding, and continuous learning.
Note though that although NLP started out with a heavily rule-based approach, this has now been largely dropped by large language models (LLMs). In a way it’s similar to conscious learning vs subconscious acquisition.
However, the underlying mechanisms differ significantly, with humans relying on cognitive and social capabilities, while computers rely on algorithms, data, and computational power.