In the context of BERT (Bidirectional Encoder Representations from Transformers) and similar transformer-based models, embeddings are used to convert raw data (like words and positions) into numerical representations that the model can process. Let's clarify what each term means:
Word Embedding:
Word embeddings are dense vector representations of words in a continuous vector space where semantically similar words are mapped to points close to each other. These embeddings capture semantic meaning, syntactic roles, and relationships between words. In BERT, each token (word or subword piece) from the input is converted into a word embedding before being processed by the model. These embeddings are learned during pre-training and are fine-tuned for downstream tasks.
Position Embedding:
Position embeddings are used to represent the position of tokens within a sequence. Since the self-attention mechanism in the Transformer model does not have an inherent sense of token order, position embeddings provide this information, enabling the model to understand word order and relative distances between words in a sentence. In BERT, position embeddings are added to the word embeddings so that the model has information about the location of each word within the sequence. BERT uses learned position embeddings which, like word embeddings, are adjusted during pre-training and can be fine-tuned for specific tasks.
Positional Encoding:
Positional encoding serves the same purpose as position embeddings but is implemented differently. While "position embeddings" usually refer to learnable parameters specific to models like BERT, "positional encoding" typically refers to fixed functions used to generate these representations. For example, in the original Transformer model proposed by Vaswani et al., positional encodings are generated using a specific set of sine and cosine functions with varying wavelengths. Unlike learned position embeddings, these encodings are not adjusted during training; they are computed externally and added to word embeddings explicitly to provide positional information.
Even though the terms "position embedding" and "positional encoding" can sometimes be used interchangeably, the key distinction lies in their learnability: position embeddings in BERT are learned parameters, while positional encodings in the original Transformer are fixed mathematical functions.
In conclusion, BERT uses word embeddings to understand the semantic meaning of words and position embeddings to capture their position within a sequence. Positional encodings, as implemented in the original Transformer model, are a non-learnable alternative to provide position information. BERT opts for learnable position embeddings to potentially capture more complex relationships and position-specific information in the input sequences.