What is the use of [SEP] in paper BERT?

Answer: [SEP] token is used in BERT to separate input segments or sentences in the input sequence.

In BERT (Bidirectional Encoder Representations from Transformers), the [SEP] token, short for “separator,” serves multiple purposes and plays a crucial role in the model’s architecture:

1. Segment Separation: BERT is designed to accept input sequences that consist of two segments or sentences. These segments could be two sentences in a question-answering task, two sentences in a text-pair classification task, or a single sentence in tasks like text generation. The [SEP] token is used to separate these segments within the input sequence. Specifically, the input to BERT is formed by concatenating the two segments together, with [SEP] tokens inserted between them to mark the boundary between segments. This allows BERT to distinguish between the different parts of the input and learn contextual representations for each segment separately.
2. Positional Encoding: In addition to segment separation, the [SEP] token also contributes to positional encoding within the input sequence. BERT, like other Transformer-based models, relies on positional encodings to convey the relative positions of tokens in the input sequence. Each token, including the [SEP] token, is associated with a unique positional embedding that represents its position in the sequence. By including [SEP] tokens to delineate segments, BERT ensures that the positional encoding accurately reflects the positions of tokens within each segment, enabling the model to capture positional information effectively.
3. End-of-Sequence Marker: In some implementations, the [SEP] token is also used as an end-of-sequence marker, indicating the end of the input sequence. This is particularly relevant in tasks where the input sequence length may vary, such as in text classification or language modeling. By appending [SEP] tokens at the end of the input sequence, BERT can explicitly identify the end of the sequence and appropriately process it during training and inference.

Overall, the [SEP] token in BERT serves as a separator between input segments, contributes to positional encoding to capture token positions accurately, and can also function as an end-of-sequence marker in certain contexts. Its inclusion in the input sequence enables BERT to effectively model relationships between segments and learn contextual representations that capture both inter-segment and intra-segment dependencies.