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Nima Keivan wants to help Amazon’s robots and fulfillment center workers to collaborate more effectively, so that robots can perform the more mundane tasks, while the humans can focus on higher-value jobs.

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

Since Rob Hyndman & Stephan Kolassa wrote their Foresight article in 2010 on “Free Open-Source Forecasting Using R” much has happened. The forecast package for the R statistical language (Hyndman & Khandakar, 2008), abbreviated to “R Forecast package” in the following, was the main focus of the article then. Now, it is the reference implementation of many classical forecasting methods such as exponential

This paper explores the potential universality of neural vocoders. We train a WaveRNN-based vocoder on 74 speakers coming from 17 languages. This vocoder is shown to be capable of generating speech of consistently good quality (98% relative mean MUSHRA when compared to natural speech) regardless of whether the input spectrogram comes from a speaker or style seen during training or from an out-of-domain

Semi-supervised learning is an efficient way to improve performance for natural language processing systems. In this work, we propose Para-SSL, a scheme to generate candidate utterances using paraphrasing and methods from semi-supervised learning. In order to perform paraphrase generation in the context of a dialog system, we automatically extract paraphrase pairs to create a paraphrase corpus. Using this

One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that already exhibit relevance (e.g., newswire headline-content pairs and encyclopedic heading-paragraph pairs). We also propose filtering techniques to eliminate

We evaluate three simple, normalization-centric changes to improve Transformer training. First, we show that pre-norm residual connections (PRENORM) and smaller initializations enable warmup-free, validation-based training with large learning rates. Second, we propose `2 normalization with a single scale parameter (SCALENORM) for faster training and better performance. Finally, we reaffirm the effectiveness

Offline handwriting recognition with deep neural networks is usually limited to words or lines due to large computational costs. In this paper, a less computationally expensive full page offline handwritten text recognition framework is introduced. This framework includes a pipeline that locates handwritten text with an object detection neural network and recognises the text within the detected regions

A key obstacle in automated analytics and metalearning is the inability to recognize when different datasets contain measurements of the same variable. Because provided attribute labels are often uninformative in practice, this task may be more robustly addressed by leveraging the data values themselves rather than just relying on their arbitrarily selected variable names. Here, we present a computationally

Formal methods are mathematically based approaches for specifying, building, and reasoning about software. Despite 50 years of research and development, formal methods have had only limited impact in industry. While we have seen success in such domains as microprocessor design and aerospace (e.g., proofs of security properties for helicopter control systems1), we have not seen wide adoption of formal methods

Although automatic classification of machine translation errors still cannot provide the same detailed granularity as manual error classification, it is an important task which enables estimation of translation errors and better understanding of the analyzed MT system, in a short time and on a large scale. State-of-the-art methods use hard decisions to assign single error labels to each word. This work

The Amazon Sustainability Data Initiative (ASDI) seeks to accelerate sustainability research and innovation by minimizing the cost and time required to acquire and analyze large sustainability datasets. ASDI supports innovators and researchers with the data, tools, and technical expertise they need to move sustainability to the next level. This repo contains docs, examples, and supporting material for ASDI

The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be targeted. In particular, if translations have to fit some given layout, quality should not only be measured in terms of adequacy and fluency, but also length. Exemplary cases are the translation of document files, subtitles, and

Optimizing placement of configuration data ensures that it’s available and consistent during “network partitions”.

Machine learning approaches for building task-oriented dialogue systems require large conversational datasets with labels to train on. We are interested in building task-oriented dialogue systems from human-human conversations, which may be available in ample amounts in existing customer care center logs or can be collected from crowd workers. Annotating these datasets can be prohibitively expensive. Recently

Today, a frame-based camera is the sensor of choice for machine vision applications. However, these cameras, originally developed for acquisition of static images rather than for sensing of dynamic uncontrolled visual environments, suffer from high power consumption, data rate, latency and low dynamic range. An event-based image sensor addresses these drawbacks by mimicking a biological retina. Instead

In the past few years, advances in artificial intelligence have captured our imaginations and led to the widespread use of voice services on our phones and in our homes.

With the launch of Alexa in 2014, Amazon sparked a renaissance in conversational AI. Combining far-field speech recognition with state-of-the-art natural language understanding, Alexa captured the imagination of millions of users worldwide. All of them had discovered that speech is, indeed, the most natural of user interfaces! In the summer of 2015, the Alexa Skills Kit (ASK) was launched with the goal

Dialogue assistants are used by millions of people today to fulfill a variety of tasks. Such assistants also serve as a digital marketplace where any developer can build a domain-specific, task-oriented, dialogue agent offering a service such as booking cabs, ordering food, listening to music, shopping etc. Also, these agents may interact with each other, when completing a task on behalf of the user. Accomplishing

Training dialog policies for speech-based virtual assistants requires a plethora of conversational data. The data collection phase is often expensive and time consuming due to human involvement. To address this issue, a common solution is to build user simulators for data generation. For the successful deployment of the trained policies into real world domains, it is vital that the user simulator mimics