Attention in NLP
Explore the role of attention mechanisms in natural language processing, specifically in neural machine translation. Learn how attention addresses unaligned data challenges, improves encoder-decoder models, and enhances translation accuracy. Understand attention scoring, normalization, and output aggregation with a practical Python example.
Let's further explore the concepts of attention mechanisms and transformers by using the example of neural machine translation (NMT). NMT is the computational approach that utilizes artificial neural networks to automatically and contextually translate text from one language to another. It’s a crucial task in natural language processing (NLP), relying on parallel datasets, denoted as X and Y, which contain sentences and their translations in different languages.
Challenge of unaligned data
One classic example of unaligned data, where there isn't a direct word-to-word correspondence between input and output sentences, is the Rosetta Stone. This alignment challenge is pivotal for machine translation but is rarely encountered.
As shown in the above example, the translation in English is primarily influenced by the black-boxed words for each element in the translated language, in this case, English. Specifically, the term "Life" is significantly impacted by the presence of 'La' and 'vie'.
The Rosetta Stone, an ancient Egyptian inscription featuring Greek, Demotic, and hieroglyphic scripts, posed a challenge in aligning diverse linguistic elements. Scholars overcame this challenge, deciphering the stone and gaining insights into ancient Egyptian hieroglyphs. Although not directly linked to modern machine translation, the Rosetta Stone highlights the complexity of aligning diverse linguistic data, emphasizing its importance in advancing language technologies.
The need for encoder-decoder models
To address the challenge of unaligned data, we employ an encoder-decoder or sequence-to-sequence model.
Typically, the encoder, implemented as a recurrent neural network (RNN), processes the entire input sequence, creating a single representation vector containing the input's information. The decoder, also an RNN, generates the output (referred to as the target sentence) while considering the final hidden state of the encoder and the previously generated output. However, this approach has limitations, especially with lengthy contexts.
Overcoming the challenge of unaligned data
This is where attention mechanisms come into play. ...