Identifying and exploring emerging trends in the news is becoming more
essential than ever with many changes occurring worldwide due to the global
health crises. However, most of the recent research has focused mainly on
detecting trends in social media, thus, benefiting from social features (e.g.
likes and retweets on Twitter) which helped the task as they can be used to
measure the engagement and diffusion rate of content. Yet, formal text data,
unlike short social media posts, comes with a longer, less restricted writing
format, and thus, more challenging. In this paper, we focus our study on
emerging trends detection in financial news articles about Microsoft, collected
before and during the start of the COVID-19 pandemic (July 2019 to July 2020).
We make the dataset accessible and propose a strong baseline (Contextual
Leap2Trend) for exploring the dynamics of similarities between pairs of
keywords based on topic modelling and term frequency. Finally, we evaluate
against a gold standard (Google Trends) and present noteworthy real-world
scenarios regarding the influence of the pandemic on Microsoft.

通过对 Microsoft 金融新闻的研究，探讨了 2019 年 7 月至 2020 年 7 月期间 COVID-19 疫情前后的新趋势检测，以主题建模和词频为基础探索关键字之间的相似性动态，并与 Google Trends 进行了评估，最终提出了一个强有力的基准 (Contextual Leap2Trend)。

金融新闻文章中新兴趋势的情境化研究

Contextualizing Emerging Trends in Financial News Articles

To improve software developers' productivity has been the holy grail of
software engineering research. But before we can claim to have improved it, we
must first be able to measure productivity. This is far from trivial. In fact,
two separate research lines on software engineers' productivity have co-existed
almost in complete isolation for a long time: automated product and process
measures on the one hand and self-reported or perceived productivity on the
other hand. In this article, we bridge the gap between the two with an
empirical study of 81 software developers at Microsoft.

本文通过研究 81 名微软软件开发人员，实证分析了自动产品和过程度量与自我报告或感知生产力之间的差距。

浅谈自动测量和自报生产力之间的关系

Mind the Gap: On the Relationship Between Automatically Measured and  Self-Reported Productivity

We Microsoft Research Asia made submissions to 11 language directions in the
WMT19 news translation tasks. We won the first place for 8 of the 11 directions
and the second place for the other three. Our basic systems are built on
Transformer, back translation and knowledge distillation. We integrate several
of our rececent techniques to enhance the baseline systems: multi-agent dual
learning (MADL), masked sequence-to-sequence pre-training (MASS), neural
architecture optimization (NAO), and soft contextual data augmentation (SCA).

微软研究亚洲在 WMT19 新闻翻译任务中使用 Transformer、反向翻译和知识蒸馏等技术并结合多智能体双向学习、掩码序列到序列预训练、神经架构优化及软性上下文数据增强等技术在 11 个语言方向中获得了 8 个方向的第一名。

微软亚洲研究院的 WMT19 系统

Microsoft Research Asia's Systems for WMT19

We describe the 2017 version of Microsoft's conversational speech recognition
system, in which we update our 2016 system with recent developments in
neural-network-based acoustic and language modeling to further advance the
state of the art on the Switchboard speech recognition task. The system adds a
CNN-BLSTM acoustic model to the set of model architectures we combined
previously, and includes character-based and dialog session aware LSTM language
models in rescoring. For system combination we adopt a two-stage approach,
whereby subsets of acoustic models are first combined at the senone/frame
level, followed by a word-level voting via confusion networks. We also added a
confusion network rescoring step after system combination. The resulting system
yields a 5.1\% word error rate on the 2000 Switchboard evaluation set.

本文介绍了微软 2017 年的对话语音识别系统，采用了基于神经网络的声学建模和语言建模的最新进展，加入了 CNN-BLSTM 声学模型和基于字符和对话会话感知的 LSTM 语言模型进行重构，采用两阶段方法进行系统合并，并在合并后通过混淆网络进行单词级投票。该系统在 Switchboard speech recognition 数据集上的评测结果显示，词错误率为 5.1％。