Text matching systems have become a fundamental service in most searching
platforms. For instance, they are responsible for matching user queries to
relevant candidate items, or rewriting the user-input query to a pre-selected
high-performing one for a better search experience. In practice, both the
queries and items often contain multiple attributes, such as the category of
the item and the location mentioned in the query, which represent condensed key
information that is helpful for matching. However, most of the existing works
downplay the effectiveness of attributes by integrating them into text
representations as supplementary information. Hence, in this work, we focus on
exploring the relationship between the attributes from two sides. Since
attributes from two ends are often not aligned in terms of number and type, we
propose to exploit the benefit of attributes by multiple-intent modeling. The
intents extracted from attributes summarize the diverse needs of queries and
provide rich content of items, which are more refined and abstract, and can be
aligned for paired inputs. Concretely, we propose a multi-intent
attribute-aware matching model (MIM), which consists of three main components:
attribute-aware encoder, multi-intent modeling, and intent-aware matching. In
the attribute-aware encoder, the text and attributes are weighted and processed
through a scaled attention mechanism with regard to the attributes' importance.
Afterward, the multi-intent modeling extracts intents from two ends and aligns
them. Herein, we come up with a distribution loss to ensure the learned intents
are diverse but concentrated, and a kullback-leibler divergence loss that
aligns the learned intents. Finally, in the intent-aware matching, the intents
are evaluated by a self-supervised masking task, and then incorporated to
output the final matching result.

通过多意图建模、属性感知编码和意图感知匹配等方法，我们提出了多意图属性感知匹配模型，旨在探索文本匹配系统中属性的有效性，以提高搜索查询与候选项的匹配效果。

搜索中的多意图属性感知文本匹配

Multi-Intent Attribute-Aware Text Matching in Searching

Binarized Neural Networks (BNNs) can significantly reduce the inference
latency and energy consumption in resource-constrained devices due to their
pure-logical computation and fewer memory accesses. However, training BNNs is
difficult since the activation flow encounters degeneration, saturation, and
gradient mismatch problems. Prior work alleviates these issues by increasing
activation bits and adding floating-point scaling factors, thereby sacrificing
BNN's energy efficiency. In this paper, we propose to use distribution loss to
explicitly regularize the activation flow, and develop a framework to
systematically formulate the loss. Our experiments show that the distribution
loss can consistently improve the accuracy of BNNs without losing their energy
benefits. Moreover, equipped with the proposed regularization, BNN training is
shown to be robust to the selection of hyper-parameters including optimizer and
learning rate.

本文提出使用分布损失来规范激活流，成功改善了二值神经网络训练中遇到的退化、饱和和梯度失配等问题，并在不影响其能量效率的同时，提高了其准确性。此外，该规范化方法还能使训练过程更加稳健。