The key to e-commerce search is how to best utilize the large yet noisy log
data. In this paper, we present our embedding-based model for grocery search at
Instacart. The system learns query and product representations with a two-tower
transformer-based encoder architecture. To tackle the cold-start problem, we
focus on content-based features. To train the model efficiently on noisy data,
we propose a self-adversarial learning method and a cascade training method.
AccOn an offline human evaluation dataset, we achieve 10% relative improvement
in RECALL@20, and for online A/B testing, we achieve 4.1% cart-adds per search
(CAPS) and 1.5% gross merchandise value (GMV) improvement. We describe how we
train and deploy the embedding based search model and give a detailed analysis
of the effectiveness of our method.

本文介绍了嵌入式模型在电商搜索中的应用，该模型由两个塔式变压器编码器构成，利用自适应对抗性学习和级联训练方法，以特别关注基于内容的特征并有效地在噪声大的数据上进行训练，检测出相对于原方案 10％的 RECALL@20 的改进并通过在线 A / B 测试达到了每次搜索中增加购物车数量（CAPS）和商品销售总值（GMV）方面 1.5％和 4.1％的提高。

基于嵌入式的美国 Instacart 超市搜索模型

An Embedding-Based Grocery Search Model at Instacart

Conventional Generative Adversarial Networks (GANs) for text generation tend
to have issues of reward sparsity and mode collapse that affect the quality and
diversity of generated samples. To address the issues, we propose a novel
self-adversarial learning (SAL) paradigm for improving GANs' performance in
text generation. In contrast to standard GANs that use a binary classifier as
its discriminator to predict whether a sample is real or generated, SAL employs
a comparative discriminator which is a pairwise classifier for comparing the
text quality between a pair of samples. During training, SAL rewards the
generator when its currently generated sentence is found to be better than its
previously generated samples. This self-improvement reward mechanism allows the
model to receive credits more easily and avoid collapsing towards the limited
number of real samples, which not only helps alleviate the reward sparsity
issue but also reduces the risk of mode collapse. Experiments on text
generation benchmark datasets show that our proposed approach substantially
improves both the quality and the diversity, and yields more stable performance
compared to the previous GANs for text generation.

提出了一种新的自我对抗学习（SAL）范式，通过使用相对鉴别器作为 GAN 的鉴别器，并引入自我改进奖励机制，成功地解决了 GAN 在生成文本方面的奖励稀疏性和模式崩溃问题，并在文本生成基准数据集上取得了更高质量和更稳定的性能表现。

使用比较歧视的自对抗学习进行文本生成

Self-Adversarial Learning with Comparative Discrimination for Text  Generation

Scalable Bayesian sampling is playing an important role in modern machine
learning, especially in the fast-developed unsupervised-(deep)-learning models.
While tremendous progresses have been achieved via scalable Bayesian sampling
such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient
descent (SVGD), the generated samples are typically highly correlated.
Moreover, their sample-generation processes are often criticized to be
inefficient. In this paper, we propose a novel self-adversarial learning
framework that automatically learns a conditional generator to mimic the
behavior of a Markov kernel (transition kernel). High-quality samples can be
efficiently generated by direct forward passes though a learned generator. Most
importantly, the learning process adopts a self-learning paradigm, requiring no
information on existing Markov kernels, e.g., knowledge of how to draw samples
from them. Specifically, our framework learns to use current samples, either
from the generator or pre-provided training data, to update the generator such
that the generated samples progressively approach a target distribution, thus
it is called self-learning. Experiments on both synthetic and real datasets
verify advantages of our framework, outperforming related methods in terms of
both sampling efficiency and sample quality.

本文提出一种新的自我对抗学习框架，用于通过学习条件生成器来生成高质量的样本，并自动学习马尔可夫内核的行为，从而逐渐逼近目标分布，从而在可扩展 Bayesian 采样和非监督深度学习模型等方面取得优势。