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Amazon Aurora is a relational database service for OLTP workloads offered as part of Amazon Web Services (AWS). In this paper, we describe the architecture of Aurora and the design considerations leading to that architecture. We believe the central constraint in high throughput data processing has moved from compute and storage to the network. Aurora brings a novel architecture to the relational database

Automatic speech recognition is now playing an important role in volume control and adjustment of modern smart speakers. According to the recognition results by using the advanced deep neural network technology, this paper proposes an efficient processing system for automatic volume control (AVC) and limiter. The theoretical analyses, subjective and objective testing results show that the proposed processing

The task of automatically detecting the end of a device-directed user request is particularly challenging in case of switching short command and long free-form utterances. While low latency end-pointing configurations typically lead to good user experiences in the case of short requests, such as “play music”, it can be too aggressive in domains with longer free-form queries, where users tend to pause noticeably

This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software Development Kit (SDK) that enables developers to extend the capabilities of Amazon’s virtual assistant, Alexa. At Amazon, the infrastructure powers over 25,000 skills deployed through the ASK, as well as AWS’s Amazon Lex SLU Service. The

Automatically understanding the plot of novels is important both for informing literary scholarship and applications such as summarization or recommendation. Various models have addressed this task, but their evaluation has remained largely intrinsic and qualitative. Here, we propose a principled and scalable framework leveraging expert-provided semantic tags (e.g., mystery, pirates) to evaluate plot representations

The ability to automatically and accurately process customer feedback is a necessity in the private sector. Unfortunately, customer feedback can be one of the most difficult types of data to work with due to the sheer volume and variety of services, products, languages, and cultures that comprise the customer experience. In order to address this issue, our team built a suite of classifiers trained on a

Rapidly growing product lines and services require a finer-granularity forecast that considers geographic locales. However the open question remains, how to assess the quality of a spatio-temporal forecast?

The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of deep learning technologies for semantic parsing is the lack of sufficient annotation training data. In this paper, we propose using sequence-to-sequence in a multi-task setup for semantic parsing with a focus on transfer learning

Topics generated by topic models are usually represented by lists of t terms or alternatively using short phrases or images. The current state-of-the-art work on labeling topics using images selects images by re-ranking a small set of candidates for a given topic. In this paper, we present a more generic method that can estimate the degree of association between any arbitrary pair of an unseen topic and

Sockeye is an open-source sequence-to-sequence framework for neural machine translation (NMT) built on PyTorch. It implements distributed training and optimized inference for state-of-the-art models, powering Amazon Translate and other MT applications. Recent developments and changes are tracked in our CHANGELOG. For a quickstart guide to training a standard NMT model on any size of data, see the WMT 2014

You can build smart home and other products that customers can control from millions of Alexa devices with just their voice. Expand your device’s capabilities with Alexa to create delightful experiences for lights, switches, thermostats, cameras, locks, and more. Plus, your Alexa-connected devices continue to become smarter with Alexa’s growing list of smart home capabilities and features that leverage

Meet the Mila team from the University of Montreal, a French-language public research university in Montreal, Quebec, Canada.

Meet the SluBot team from UC Santa Cruz, a public land-grant research university in Santa Cruz, California.

Picture walking into your house after a long day. As you step in, your favorite music starts playing in the background and the lights dim to suit the weather outside.

We're an eclectic team of research-oriented undergraduate and graduate students in Princeton's CS and math departments.

Sounding Board is a conversational agent that engages users through thoughtful and informative discussion.

Meet the EmersonBot team from Emory University, a private research university in Atlanta, Georgia, US.

The UC Berkeley team consists of William, Phillip, Piyush, and James, and is the only fully undergraduate team in the competition.

This team, consisting of PhD and Masters students from Carnegie Mellon University, brings together experience in chatbot interaction strategies, question answering, neural modeling and machine learning.

We are the Alquist team from CTU, Prague, Czech Republic.