Extended Transformer Construction, also known as ETC, is an enhanced version of the Transformer architecture that utilizes a new attention mechanism to extend the original in two main ways: (1) it allows for a larger input length, up to several thousands, and (2) it can process structured inputs as well as sequential ones.
What is ETC?
The Transformer architecture is a machine learning model used for natural language processing tasks such as translation and summarization. The original Transformer had a fixed input length of 512 tokens, which limited its functionality. ETC extends the Transformer's capabilities by allowing for much larger input lengths and the processing of structured inputs.
The key ideas that enable ETC to achieve these improvements are a new global-local attention mechanism and relative position encodings. The attention mechanism in ETC determines how much focus each element of the input should receive based on its relevance to the output. The global-local attention mechanism introduces a way to consider both global and local contexts of the input while keeping the computational cost low. The relative position encodings add positional information to the input, further enhancing the attention mechanism's effectiveness.
ETC in Action
One significant advantage of ETC is that it can lift weights from existing BERT models, which can save computational resources during training. BERT is a state-of-the-art Transformer-based model used for various natural language processing tasks, such as question answering and language modeling.
ETC is especially suitable for tasks that require processing of structured or hierarchically organized inputs. For example, ETC can be used for the task of translating product manuals, where the input is structured with headings and subheadings. ETC can also be used to process web pages, which contain hierarchical structures, in areas such as information retrieval and web scraping.
Advantages of ETC
One of the advantages of ETC over the original Transformer architecture is its ability to process structured inputs. This enables ETC to handle a broader range of natural language processing tasks, such as parsing and semantic analysis.
The global-local attention mechanism also allows ETC to consider both local and global context while keeping the computational cost low. This feature allows ETC to better understand the text's meaning by considering the immediate context and the document as a whole.
Another advantage of ETC is its ability to process much larger inputs than the original Transformer, which was limited to 512 tokens. This expanded input length can be beneficial for many natural language processing tasks, such as summarization and classification, where the full context of the input is necessary to make accurate predictions.
ETC is an enhanced version of the Transformer architecture that can process structured inputs and larger input lengths than the original. It achieves these improvements through a new attention mechanism and relative position encodings. ETC is highly suitable for tasks that require processing structured or hierarchically organized inputs, such as web scraping or information retrieval. With its ability to handle larger inputs and understand the context better, ETC has the potential to significantly improve many natural language processing tasks in the future.
