Choosing between character-based language models and n-gram/word-based language models depends on the specific task, dataset, and requirements of your application. Here’s a detailed comparison of the two approaches to help determine which is better for your needs:
1. Character-Based Language Models
Character-based models treat individual characters as the fundamental unit of input and output.
Advantages:
Granular Understanding:
Handles rare and out-of-vocabulary (OOV) words better, as all words are made of characters.
Effective for morphologically rich languages (e.g., Finnish, Turkish), where word-based approaches may struggle with inflections and derivations.
Compact Vocabulary:
The vocabulary size is small (just the set of characters, typically ~30–150 depending on the language), making it memory-efficient.
Generalization:
Can generate entirely new words by learning patterns at the character level, which is useful in creative or domain-specific tasks.
Disadvantages:
Longer Sequences:
Sentences become longer when encoded as characters, making training slower and requiring more computational resources.
Lack of Semantic Context:
Characters alone don’t convey meaning. Models must learn higher-level abstractions to understand and generate meaningful sequences.
More Training Data:
Needs a lot of data to generalize patterns effectively since it doesn’t have built-in word-level semantic structures.
Use Cases:
Tasks with creative text generation (e.g., poetry, brand names).
Languages with rich morphology or no clear word boundaries (e.g., Chinese).
Domains with a lot of unknown words or abbreviations (e.g., biomedical text).
2. N-Gram/Word-Based Language Models
Word-based models use words or fixed-length sequences of words (n-grams) as their input units.
Advantages:
Semantic Understanding:
Words naturally encapsulate meaning, making it easier for models to leverage semantic relationships and syntactic structure.
Efficient Representation:
Word-based inputs are shorter in length compared to character-based ones, leading to faster training and inference.
Pre-trained Embeddings:
Existing pre-trained embeddings like Word2Vec, GloVe, or FastText provide semantic-rich representations, reducing the need for massive data during training.
Disadvantages:
Out-of-Vocabulary Problem:
Cannot handle unknown words that are not in the vocabulary unless subword techniques are employed (e.g., byte-pair encoding).
Large Vocabulary:
Vocabulary size grows with dataset size, requiring more memory and computational resources.
Poor at Morphological Variations:
Struggles with words that have multiple forms (e.g., "run," "running," "ran") unless augmented with stemming or lemmatization.
Use Cases:
Applications that require strong semantic understanding (e.g., text classification, summarization).
Tasks where input vocabulary is well-defined or not too large (e.g., product reviews, FAQs).
Scenarios where pre-trained embeddings are advantageous.
Which is Better?
Criterion | Character-Based | N-Gram/Word-Based
-- | -- | --
Handling OOV words | Excellent | Poor (unless using subword techniques)
Model size and efficiency | Smaller vocabulary but longer sequences | Larger vocabulary but shorter sequences
Semantic understanding | Weak, needs to learn from scratch | Stronger, especially with pre-trained embeddings
Data requirements | Needs more data to learn character-level patterns | More efficient with limited data
Generalization | Can generate unseen words | Limited unless using subword or subphrase methods
Hybrid Approach: Subword Models
Modern language models (e.g., BERT, GPT) combine the strengths of both by using subword tokenization (e.g., byte-pair encoding or WordPiece):
Breaks text into meaningful units, such as frequent words and word fragments.
Balances efficiency, semantic richness, and generalization.
Subword Tokenization Examples:
Sentence: "unbelievable"
Character-based: u, n, b, e, l, i, e, v, a, b, l, e
Word-based: unbelievable
Subword-based: un, ##believable
Conclusion
Character-Based: Best for tasks requiring robust handling of rare or unseen words, creative generation, or morphologically complex languages.
Word-Based (or N-Gram): Best for tasks requiring semantic understanding, efficiency, and leveraging pre-trained word embeddings.
Subword-Based: Often the most practical and widely used in state-of-the-art NLP models today, as it balances both approaches.
Choosing between character-based language models and n-gram/word-based language models depends on the specific task, dataset, and requirements of your application. Here’s a detailed comparison of the two approaches to help determine which is better for your needs:
1. Character-Based Language Models
Character-based models treat individual characters as the fundamental unit of input and output.
Advantages:
Disadvantages:
Use Cases:
2. N-Gram/Word-Based Language Models
Word-based models use words or fixed-length sequences of words (n-grams) as their input units.
Advantages:
Disadvantages:
Use Cases:
Which is Better?
Criterion | Character-Based | N-Gram/Word-Based -- | -- | -- Handling OOV words | Excellent | Poor (unless using subword techniques) Model size and efficiency | Smaller vocabulary but longer sequences | Larger vocabulary but shorter sequences Semantic understanding | Weak, needs to learn from scratch | Stronger, especially with pre-trained embeddings Data requirements | Needs more data to learn character-level patterns | More efficient with limited data Generalization | Can generate unseen words | Limited unless using subword or subphrase methodsHybrid Approach: Subword Models
Modern language models (e.g., BERT, GPT) combine the strengths of both by using subword tokenization (e.g., byte-pair encoding or WordPiece):
Subword Tokenization Examples:
u,n,b,e,l,i,e,v,a,b,l,eunbelievableun,##believableConclusion