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Inspired by studies on the overwhelming presence of experience-sharing in human- human conversations, Emora, the social chatbot developed by Emory University, aims to bring such experience-focused interaction to the current field of conver- sational AI. The traditional approach of information-sharing topic handlers is balanced with a focus on opinion-oriented exchanges that Emora delivers, and new conversational

Building conversational systems that enable natural language interactions with machines has been an attractive research area since the early days of computing, as exemplified by earlier text-based systems such as ELIZA [Weizenbaum, 1966]. The research and development work in this area has been increasing since then, with many publications on both task-oriented [Hosseini-Asl et al, 2020; Papangelis et al

We describe the ZOTBOT system for open-ended conversations, designed for the Alexa Prize competition. We focus on two main shortcomings in existing conversational agents: lack of awareness in commonsense reasoning when responding to user utterances (resulting in nonsensical or uninteresting responses) and inability to understand semantics and converse naturally about fact-based articles in a compelling

Today I am happy to announce the public release of the Topical Chat Dataset, a text-based collection of more than 235,000 utterances (over 4,700,000 words) that will help support high-quality, repeatable research in the field of dialogue systems.

One of the ways that we’re always trying to improve Alexa’s performance is by teaching her to ignore speech that isn’t intended for her. At this year’s International Conference on Acoustics, Speech, and Signal Processing, my colleagues and I will present a new technique for doing this, which could complement the techniques that Alexa already uses.

We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning. By parametrizing a per-time-series linear state space model with a jointly-learned recurrent neural network, our method retains desired properties of state space models such as data efficiency and interpretability, while making use of the ability to learn complex patterns from raw data

Although deep neural networks have enabled accurate large-vocabulary speech recognition, training them requires thousands of hours of transcribed data, which is time-consuming and expensive to collect. So Amazon scientists have been investigating techniques that will let Alexa learn with minimal human involvement, techniques that fall in the categories of unsupervised and semi-supervised learning.

To ensure that Alexa Prize contestants can concentrate on dialogue systems — the core technology of socialbots — Amazon scientists and engineers built a set of machine learning modules that handle fundamental conversational tasks and a development environment that lets contestants easily mix and match existing modules with those of their own design.

New approach to reference resolution rewrites queries to clarify ambiguous references.

Amazon SageMaker Debugger automates the debugging process of machine learning training jobs. From training jobs, Debugger allows you to run your own training script (Zero Script Change experience) using Debugger built-in features—Hook and Rule—to capture tensors, have flexibility to build customized Hooks and Rules for configuring tensors as you want, and make the tensors available for analysis by saving

Building a dialogue system able to talk fluently and meaningfully in an open domain conversation is one of the foundational challenges in the field of AI. Recent progress in NLP driven by the application of the deep neural networks and large language models opened new possibilities to solve many hard problems of the conversational AI. Alexa Prize Socialbot Grand Challenge gives a unique opportunity to test

The building block of Transformer can be seen as inducing message passing over a complete graph whose nodes correspond to input tokens. Such dense connections make the Transformer data-hungry. Star-Transformer exploits short-term dependencies more heavily by keeping the connections between adjacent tokens but relaying long dependencies via a central node, thereby reducing the number of connections from

In this paper we address the problem of detecting previously unseen novel audio events in the presence of real-life acoustic backgrounds. Specifically, during training, we learn subspaces corresponding to each acoustic background, and during testing the audio frame in question is decomposed into a component that lies on the mixture of subspaces and a super gaussian outlier component.Based on the energy

Embedding entity names from diverse skills in a shared representations space enables system to suggest neglected entity names with 88.5% accuracy.

Earlier this year, at Amazon’s re:MARS conference, Alexa head scientist Rohit Prasad unveiled Alexa Conversations, a new service that allows Alexa skill developers to more easily integrate conversational elements into their skills. The announcement is an indicator of the next stage in Alexa’s evolution: more-natural, dialogue-based engagements that enable Alexa to aggregate data and refine requests to better meet customer needs.

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.

When a customer asks Alexa to play “Hey Jude”, and Alexa responds, “Playing 'Hey Jude' by the Beatles,” that response is generated by a text-to-speech (TTS) system, which converts textual inputs into synthetic-speech outputs...

In recent years, data representation has emerged as an important research topic within machine learning.

Computerized question-answering systems usually take one of two approaches. Either they do a text search and try to infer the semantic relationships between entities named in the text, or they explore a hand-curated knowledge graph, a data structure that directly encodes relationships among entities.

At a recent press event on Alexa's latest features, Alexa’s head scientist, Rohit Prasad, mentioned multistep requests in one shot, a capability that allows you to ask Alexa to do multiple things at once. For example, you might say, “Alexa, add bananas, peanut butter, and paper towels to my shopping list.” Alexa should intelligently figure out that “peanut butter” and “paper towels” name two items, not four, and that bananas are a separate item.