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Just as Alexa can wake up without the need to press a button, she also automatically detects when a user finishes her query and expects a response. This task is often called “end-of-utterance detection,” “end-of-query detection,” “end-of-turn detection,” or simply “end-pointing.”

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

Team SlugBot is proud to represent the Natural Language and Dialogue Systems laboratory at UC Santa Cruz.

Our international team of 8 PhD students and faculty advisors has a wide range of experience from both academic and industrial research.

We are EVE, a team of researchers from the Brigham Young University Perception, Control, and Cognition Laboratory, and we've been accepted to the Amazon Alexa Prize Challenge.

We are the Tartan team from Carnegie Mellon University; a diverse team of researchers interested in advancing the state of conversational AI.

Named for our university's mascot, Gunrock, our team is a group of students who all share a passion for improving everyday human experiences through artificial intelligence.

KTH has a long history of research on conversational systems. Our team of graduate and undergraduate students has a wide range of expertise in linguistics, cognitive science, machine learning and artificial intelligence.

We are a team of student researchers from the IRLab at Emory University with enthusiasm in the advancement of conversational AI.

A challenge for speech recognition for voice-controlled household devices, like the Amazon Echo or Google Home, is robustness against interfering background speech. Formulated as a far-field speech recognition problem, another person or media device in proximity can produce background speech that can interfere with the device-directed speech. We expand on our previous work on device-directed speech detection

Supplementing log filter-bank energies with i-vectors is a popular method for adaptive training of deep neural network acoustic models. While offline i-vectors (the target utterance or other relevant adaptation material is available for i-vector extraction prior to decoding) have been well studied, there is little analysis of online i-vectors and their robustness in multi-user scenarios where speaker changes

We present a platform built on large-scale, data-centric machine learning (ML) approaches, whose particular focus is demand forecasting in retail. At its core, this platform enables the training and application of probabilistic demand forecasting models, and provides convenient abstractions and support functionality for forecasting problems. The platform comprises of a complex end-to-end machine learning

We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine translation from weak user feedback instead of human references or post-edits. On each of

Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL). Additionally, as companies accumulate massive quantities of dialog transcripts between customers and trained human agents, encoder-decoder methods have gained popularity as agent utterances can be directly treated as supervision without the need for utterance-level

Optimization is commonly employed to determine the content of web pages, such as to maximize conversions on landing pages or click-through rates on search engine result pages. Often the layout of these pages can be decoupled into several separate decisions. For example, the composition of a landing page may involve deciding which image to show, which wording to use, what color background to display, etc

We present a lightweight system to extract, store and manage metadata and provenance information of common artifacts in machine learning (ML) experiments: datasets, models, predictions, evaluations and training runs. Our system accelerates users in their ML workflow, and provides a basis for comparability and repeatability of ML experiments. We achieve this by tracking the lineage of produced artifacts

Randomized algorithms are central to modern machine learning. In the presence of massive datasets, researchers often turn to stochastic optimization to solve learning problems. Of particular interest is stochastic gradient descent (SGD), a first-order method that approximates the learning objective and gradient by a random point estimate. A classical question in learning theory is, if a randomized learner

Topics generated by topic models are typically presented as a list of topic terms. Automatic topic labelling is the task of generating a succinct label that summarises the theme or subject of a topic, with the intention of reducing the cognitive load of end-users when interpreting these topics. Traditionally, topic label systems focus on a single label modality, e.g. textual labels. In this work we propose

E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell billions of products. Machine learning (ML) algorithms involving products are often used to improve the customer experience and increase revenue, e.g., product similarity, recommendation, and price estimation. The products are required to be represented as features before training an ML algorithm. In this paper, we propose an approach

Topical PageRank (TPR) uses latent topic distribution inferred by Latent Dirichlet Allocation (LDA) to perform ranking of noun phrases extracted from documents. The ranking procedure consists of running PageRank K times, where K is the number of topics used in the LDA model. In this paper, we propose a modification of TPR, called Salience Rank. Salience Rank only needs to run PageRank once and extracts