This paper introduces WeChat's participation in WMT 2022 shared biomedical
translation task on Chinese to English. Our systems are based on the
Transformer, and use several different Transformer structures to improve the
quality of translation. In our experiments, we employ data filtering, data
generation, several variants of Transformer, fine-tuning and model ensemble.
Our Chinese$\to$English system, named Summer, achieves the highest BLEU score
among all submissions.

本文介绍了微信参与 WMT 2022 共享生物医学翻译任务的情况，我们基于 Transformer 模型构建系统，采用数据过滤、数据增强、多种 Transformer 变体、微调和模型集成等方法来提高翻译质量，其中我们的中文至英文系统名为 Summer，在所有提交作品中获得最高 BLEU 分数。

WMT22 生物医学翻译任务的微信神经机器翻译系统

Summer: WeChat Neural Machine Translation Systems for the WMT22 Biomedical Translation Task

WeChat is the largest social instant messaging platform in China, with 1.1
billion monthly active users. "Top Stories" is a novel friend-enhanced
recommendation engine in WeChat, in which users can read articles based on
preferences of both their own and their friends. Specifically, when a user
reads an article by opening it, the "click" behavior is private. Moreover, if
the user clicks the "wow" button, (only) her/his direct connections will be
aware of this action/preference. Based on the unique WeChat data, we aim to
understand user preferences and "wow" diffusion in Top Stories at different
levels. We have made some interesting discoveries. For instance, the "wow"
probability of one user is negatively correlated with the number of connected
components that are formed by her/his active friends, but the click probability
is the opposite. We further study to what extent users' "wow" and click
behavior can be predicted from their social connections. To address this
problem, we present a hierarchical graph representation learning based model
DiffuseGNN, which is capable of capturing the structure-based social
observations discovered above. Our experiments show that the proposed method
can significantly improve the prediction performance compared with alternative
methods.

本文讲述了 WeChat 平台的 Top Stories 推荐引擎以及用户之间的朋友增强型阅读体验，分析了用户的喜好和 “wow” 行为的传播。研究结果发现，用户的 “wow” 概率与其连接的个数成反比，而单纯点击文章的概率则相反。研究者考虑到模型预测结果的需求，提出了 DiffuseGNN 模型。实验结果表明，该模型可以显著提高预测性能，具有实际应用价值。

理解微信用户偏好与 “Wow” 传播

Understanding WeChat User Preferences and "Wow" Diffusion

Social instant messaging services are emerging as a transformative form with
which people connect, communicate with friends in their daily life - they
catalyze the formation of social groups, and they bring people stronger sense
of community and connection. However, research community still knows little
about the formation and evolution of groups in the context of social messaging
- their lifecycles, the change in their underlying structures over time, and
the diffusion processes by which they develop new members. In this paper, we
analyze the daily usage logs from WeChat group messaging platform - the largest
standalone messaging communication service in China - with the goal of
understanding the processes by which social messaging groups come together,
grow new members, and evolve over time. Specifically, we discover a strong
dichotomy among groups in terms of their lifecycle, and develop a separability
model by taking into account a broad range of group-level features, showing
that long-term and short-term groups are inherently distinct. We also found
that the lifecycle of messaging groups is largely dependent on their social
roles and functions in users' daily social experiences and specific purposes.
Given the strong separability between the long-term and short-term groups, we
further address the problem concerning the early prediction of successful
communities. In addition to modeling the growth and evolution from group-level
perspective, we investigate the individual-level attributes of group members
and study the diffusion process by which groups gain new members. By
considering members' historical engagement behavior as well as the local social
network structure that they embedded in, we develop a membership cascade model
and demonstrate the effectiveness by achieving AUC of 95.31% in predicting
inviter, and an AUC of 98.66% in predicting invitee.

通过分析中国最大的独立消息通信服务 WeChat 的日常使用日志，本文旨在理解社交消息群组如何相互联系，增长新成员并随着时间的推移发展。我们发现组的生命周期存在很强的二分法，它们的演化在很大程度上取决于它们在用户每日社交体验和特定目的中的社交角色和功能。此外，本文还研究了群成员的个体属性，并研究了群获得新成员的扩散过程，开发了一个会员级联模型并证明了其在预测邀请者和邀请接受者方面的有效性。