3,724 research outputs found
Attention Is All You Need
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.Comment: 15 pages, 5 figure
Simple Recurrent Units for Highly Parallelizable Recurrence
Common recurrent neural architectures scale poorly due to the intrinsic
difficulty in parallelizing their state computations. In this work, we propose
the Simple Recurrent Unit (SRU), a light recurrent unit that balances model
capacity and scalability. SRU is designed to provide expressive recurrence,
enable highly parallelized implementation, and comes with careful
initialization to facilitate training of deep models. We demonstrate the
effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over
cuDNN-optimized LSTM on classification and question answering datasets, and
delivers stronger results than LSTM and convolutional models. We also obtain an
average of 0.7 BLEU improvement over the Transformer model on translation by
incorporating SRU into the architecture.Comment: EMNL
Language Modeling with Deep Transformers
We explore deep autoregressive Transformer models in language modeling for
speech recognition. We focus on two aspects. First, we revisit Transformer
model configurations specifically for language modeling. We show that well
configured Transformer models outperform our baseline models based on the
shallow stack of LSTM recurrent neural network layers. We carry out experiments
on the open-source LibriSpeech 960hr task, for both 200K vocabulary word-level
and 10K byte-pair encoding subword-level language modeling. We apply our
word-level models to conventional hybrid speech recognition by lattice
rescoring, and the subword-level models to attention based encoder-decoder
models by shallow fusion. Second, we show that deep Transformer language models
do not require positional encoding. The positional encoding is an essential
augmentation for the self-attention mechanism which is invariant to sequence
ordering. However, in autoregressive setup, as is the case for language
modeling, the amount of information increases along the position dimension,
which is a positional signal by its own. The analysis of attention weights
shows that deep autoregressive self-attention models can automatically make use
of such positional information. We find that removing the positional encoding
even slightly improves the performance of these models.Comment: To appear in the proceedings of INTERSPEECH 201
Efficient Attention: Attention with Linear Complexities
Dot-product attention has wide applications in computer vision and natural
language processing. However, its memory and computational costs grow
quadratically with the input size. Such growth prohibits its application on
high-resolution inputs. To remedy this drawback, this paper proposes a novel
efficient attention mechanism equivalent to dot-product attention but with
substantially less memory and computational costs. Its resource efficiency
allows more widespread and flexible integration of attention modules into a
network, which leads to better accuracies. Empirical evaluations demonstrated
the effectiveness of its advantages. Efficient attention modules brought
significant performance boosts to object detectors and instance segmenters on
MS-COCO 2017. Further, the resource efficiency democratizes attention to
complex models, where high costs prohibit the use of dot-product attention. As
an exemplar, a model with efficient attention achieved state-of-the-art
accuracies for stereo depth estimation on the Scene Flow dataset. Code is
available at https://github.com/cmsflash/efficient-attention.Comment: To appear at WACV 202
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