What is an attention mechanism?
Attention Mechanism is also an attempt to implement the same action of selectively concentrating on a few relevant things, while ignoring others in deep neural networks.
What is word alignment in machine translation?
Word alignment is an important component of a complete statistical machine translation (SMT) pipeline. The objective of the word alignment task is to discover the word-to-word correspondences in a sentence pair. Models for word alignment depend on the way they decompose this problem.
What is word aligned?
Words are said to be Aligned in memory if they begin at a byte-address that is a multiple of the number of bytes in a word. • For example, If the word length is 16(2 bytes), aligned words begin at byte-addresses 0, 2, 4 . . . . .
What is translation alignment?
Alignment is a process of matching source and target segments of text in order to create a translation memory. The purpose of alignment is to capture relations of equivalence or correspondence in a translation.
How is attention used in neural machine translation?
Attention mechanisms are being increasingly used to improve the performance of Neural Machine Translation (NMT) by selectively focusing on sub-parts of the sentence during translation.
What are the two types of attention mechanisms?
In this post, we will cover 2 simple types of attention mechanism: A global approach (which attends to all source words) and A local approach (which only looks at a subset of source words). Do keep it in mind that source refers to encoder and target refer to the decoder.
How is NMT used in large scale translation?
NMT has achieved the state of the art performances in large scale translation tasks like English to French/German. NMT requires minimal domain knowledge and is conceptually very simple. NMT has a small memory footprint as it does not store gigantic phase tables and language models. NMT has the ability to generalize well to very long word sentences.
How does machine learning predict a target word?
Each time the proposed model generates a word in a translation, it (soft-)searches for a set of positions in a source sentence where the most relevant information is concentrated. The model then predicts a target word based on the context vectors associated with these source positions and all the previous generated target words.