Transformer-based models have achieved great success on sentence pair
modeling tasks, such as answer selection and natural language inference (NLI).
These models generally perform cross-attention over input pairs, leading to
prohibitive computational costs. Recent studies propose dual-encoder and late
interaction architectures for faster computation. However, the balance between
the expressive of cross-attention and computation speedup still needs better
coordinated. To this end, this paper introduces a novel paradigm MixEncoder for
efficient sentence pair modeling. MixEncoder involves a light-weight
cross-attention mechanism. It conducts query encoding only once while modeling
the query-candidate interaction in parallel. Extensive experiments conducted on
four tasks demonstrate that our MixEncoder can speed up sentence pairing by
over 113x while achieving comparable performance as the more expensive
cross-attention models.

本文提出了一种高效的 MixEncoder 范式，它包括一种轻量级的交叉关注机制，可以在快速计算的同时实现可比较的性能，大大加速了句子配对。

一次就够：一种轻量级的交叉注意力用于快速语句对建模

Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling

Deep neural networks have enabled progress in a wide variety of applications.
Growing the size of the neural network typically results in improved accuracy.
As model sizes grow, the memory and compute requirements for training these
models also increases. We introduce a technique to train deep neural networks
using half precision floating point numbers. In our technique, weights,
activations and gradients are stored in IEEE half-precision format.
Half-precision floating numbers have limited numerical range compared to
single-precision numbers. We propose two techniques to handle this loss of
information. Firstly, we recommend maintaining a single-precision copy of the
weights that accumulates the gradients after each optimizer step. This
single-precision copy is rounded to half-precision format during training.
Secondly, we propose scaling the loss appropriately to handle the loss of
information with half-precision gradients. We demonstrate that this approach
works for a wide variety of models including convolution neural networks,
recurrent neural networks and generative adversarial networks. This technique
works for large scale models with more than 100 million parameters trained on
large datasets. Using this approach, we can reduce the memory consumption of
deep learning models by nearly 2x. In future processors, we can also expect a
significant computation speedup using half-precision hardware units.

介绍了一种使用半精度浮点数训练深度神经网络的技术，通过减少内存消耗和利用未来处理器的半精度硬件单元，大幅提高模型训练的计算速度。