Attention is an operation that selects some largest element from some set, where the notion of largest is defined elsewhere. Applying this operation to sequence to sequence mapping results in significant improvements to the task at hand. In this paper we provide the mathematical definition of attention and examine its application to sequence to sequence models. We highlight the exact correspondences between machine learning implementations of attention and our mathematical definition. We provide clear evidence of effectiveness of attention mechanisms evaluating models with varying degrees of attention on a very simple task: copying a sentence. We find that models that make greater use of attention perform much better on sequence to sequence mapping tasks, converge faster and are more stable.
The authors in @cite_11 compare three different variants of encoder-decoder models: two attention-based models and a character sequence to sequence model. They use data from Twitter Typo Curpose and Combilex data set which contains English graphemes to phonetic representation mappings. They compare these models against specialized monotone seq2seq models, and find them to be comparable in terms of performance. However, they did not look at the transformer or CNN architectures, nor did they use the same sentence input-output scheme that we propose here. In @cite_13 authors propose taxonomy of attention where they classify different models based on number of sequences, number of abstraction levels, number of positions and number of representations. They also point out the distinct network architectures that use attention. These include: encoder-decoder, memory networks, networks with RNNs. In @cite_3 authors group attention models based on tasks addressed'' classification. These include things like document classification, machine translation, morphology, information extraction, syntax etc. A unified attention model'' is introduced which is similar to Transformer model. The authors propose a taxonomy of attention models based on input representation, compatibility functions, distribution functions, multiplicity.
@cite_13 @cite_3 @cite_11
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Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we propose a taxonomy which groups existing techniques into coherent categories. We review the different neural architectures in which attention has been incorporated, and also show how attention improves interpretability of neural models. Finally, we discuss some applications in which modeling attention has a significant impact. We hope this survey will provide a succinct introduction to attention models and guide practitioners while developing approaches for their applications. Attention is an increasingly popular mechanism used in a wide range of neural architectures. Because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures for natural language processing, with a focus on architectures designed to work with vector representation of the textual data. We discuss the dimensions along which proposals differ, the possible uses of attention, and chart the major research activities and open challenges in the area. We analyze the performance of encoder-decoder neural models and compare them with well-known established methods. The latter represent different classes of traditional approaches that are applied to the monotone sequence-to-sequence tasks OCR post-correction, spelling correction, grapheme-to-phoneme conversion, and lemmatization. Such tasks are of practical relevance for various higher-level research fields including digital humanities, automatic text correction, and speech recognition. We investigate how well generic deep-learning approaches adapt to these tasks, and how they perform in comparison with established and more specialized methods, including our own adaptation of pruned CRFs.