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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Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit "exposure bias" - they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries. Sequence-to-sequence attention-based models have recently shown very promising results on automatic speech recognition (ASR) tasks, which integrate an acoustic, pronunciation and language model into a single neural network. In these models, the Transformer, a new sequence-to-sequence attention-based model relying entirely on self-attention without using RNNs or convolutions, achieves a new single-model state-of-the-art BLEU on neural machine translation (NMT) tasks. Since the outstanding performance of the Transformer, we extend it to speech and concentrate on it as the basic architecture of sequence-to-sequence attention-based model on Mandarin Chinese ASR tasks. Furthermore, we investigate a comparison between syllable based model and context-independent phoneme (CI-phoneme) based model with the Transformer in Mandarin Chinese. Additionally, a greedy cascading decoder with the Transformer is proposed for mapping CI-phoneme sequences and syllable sequences into word sequences. Experiments on HKUST datasets demonstrate that syllable based model with the Transformer performs better than CI-phoneme based counterpart, and achieves a character error rate (CER) of , which is competitive to the state-of-the-art CER of @math by the joint CTC-attention based encoder-decoder network. Current methods for video description are based on encoder-decoder sentence generation using recurrent neural networks (RNNs). Recent work has demonstrated the advantages of integrating temporal attention mechanisms into these models, in which the decoder network predicts each word in the description by selectively giving more weight to encoded features from specific time frames. Such methods typically use two different types of features: image features (from an object classification model), and motion features (from an action recognition model), combined by naive concatenation in the model input. Because different feature modalities may carry task-relevant information at different times, fusing them by naive concatenation may limit the model's ability to dynamically determine the relevance of each type of feature to different parts of the description. In this paper, we incorporate audio features in addition to the image and motion features. To fuse these three modalities, we introduce a multimodal attention model that can selectively utilize features from different modalities for each word in the output description. Combining our new multimodal attention model with standard temporal attention outperforms state-of-the-art methods on two standard datasets: YouTube2Text and MSR-VTT. The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.