Overview of Relative Position Encodings
Relative Position Encodings are a type of position embeddings used in Transformer-based models to capture pairwise, relative positional information. They are essential in various natural language processing tasks, including language modeling and machine translation.
In a traditional transformer, absolute positional information is used to calculate the attention scores between tokens. However, this approach is limited as it does not differentiate between tokens that are far apart in a sequence but still have a contextually relevant relationship. For instance, in machine translation, the same word may appear in different positions in the source and target sentences, and its position is a crucial factor in determining the translation output.
Relative Position Encodings address this limitation by incorporating pairwise, relative positional information into the self-attention mechanism. This extra information is supplied as a subcomponent of the keys and values matrices, enabling the model to capture the contextual relevance between tokens more accurately.
How Relative Position Encodings Work
The primary difference between traditional embeddings and relative position encodings lies in how they use positional information to calculate attention scores.
In a traditional transformer, absolute positional information is added to the query, key, and value embeddings before calculating the attention scores using the following equation:
Here, i represents the query token, j represents the key token, and PE represents the positional encoding. The positional encoding is a vector that encodes the position of the token in the sequence.
On the other hand, Relative Position Encodings add an extra component to the query, key, and value embeddings to capture the pairwise, relative positional information between the tokens. The equation to calculate attention scores using Relative Position Encodings is shown below:
Here, k is the extra vector added to the key embedding to capture relative positional information, Vr is the extra vector added to the value embedding, and f is a function that calculates the similarity between the query and the modified key embeddings.
The extra vectors k and Vr represent the relative position with respect to the query token, and they are generated based on the distance between the query and the key tokens. The distance can be represented using either an absolute or a relative position index.
Overall, Relative Position Encodings enable the transformer to capture the relative context between two tokens apart from their absolute position in an input sequence.
Applications of Relative Position Encodings
Relative Position Encodings have numerous applications in natural language processing tasks, especially in tasks that require capturing long-term dependencies and contextual relationships between tokens. Here are some common applications of Relative Position Encodings:
Language Modeling
Language modeling is the task of predicting the probability distribution of the next token given the previous tokens in a sequence. Relative Position Encodings help improve language modeling performance by capturing the relative relationships between tokens that are far apart in the sequence.
Machine Translation
Machine translation is the task of translating text from one language to another. In machine translation, Relative Position Encodings help capture the contextual relevance of tokens in the source and target languages by incorporating pairwise, relative positional information into the self-attention mechanism.
Question Answering
Question Answering is the task of answering questions based on a given context. In this task, Relative Position Encodings help the model capture the relationships between the question and the context, improving the accuracy of the answer.
Limitations of Relative Position Encodings
While Relative Position Encodings offer significant performance improvements in various natural language processing tasks, they are not entirely without limitations.
One significant limitation of Relative Position Encodings is that they require additional computational resources and memory, increasing the model's overall complexity. This increased complexity can lead to longer training times and higher inference costs.
Another limitation of Relative Position Encodings is that they may not be suitable for all natural language processing tasks. For instance, in tasks where the position of a token is not critical in determining its context, using Relative Position Encodings may not significantly improve performance.
Relative Position Encodings are an essential technique used to capture pairwise, relative positional information in Transformer-based models. By incorporating relative positional information into the attention mechanism, these models can better capture contextual relationships between tokens, leading to improved performance on various natural language processing tasks.
Despite their limitations, Relative Position Encodings are an exciting development in the field of natural language processing and have the potential to revolutionize the way we process and understand language.
