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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 present Chirpy Cardinal, an open-domain dialogue agent, as a research plat- form for the 2019 Alexa Prize competition. Building an open-domain socialbot that talks to real people is challenging – such a system must meet multiple user expectations such as broad world knowledge, conversational style, and emotional connection. Our socialbot engages users on their terms – prioritizing their interests, feelings

The third version of the open-domain dialogue system Alquist developed within the Alexa Prize 2020 competition is designed to conduct coherent and engaging conversations on popular topics. The main novel contribution is the introduction of a system leveraging an innovative approach based on a conversational knowledge graph and adjacency pairs. The conversational knowledge graph allows the system to utilize

Gunrock 2.0 is built on top of Gunrock with an emphasis on user adaptation. Gunrock 2.0 combines various neural natural language understanding modules, including named entity detection, linking, and dialog act prediction, to improve user understanding. Its dialog management is a hierarchical model that handles various topics, such as movies, music, and sports. The system-level dialog manager can handle

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

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

Conversational Intelligence requires that a person engage on informational, personal and relational levels. Advances in Natural Language Understanding have helped recent chatbots succeed at dialog on the informational level. However, current techniques still lag for conversing with humans on a personal level and fully relating to them. The University of Michigan’s submission to the Alexa Prize Grand Challenge

The need for high-quality, large-scale, goal-oriented dialogue datasets continues to grow as virtual assistants become increasingly widespread. However, existing publicly available datasets useful for this area are limited either in their size, linguistic diversity, domain coverage, or annotation granularity. We introduce the MultiDoGO dataset to overcome these limitations. With a total of over 65,000 dialogues

We introduce a method to generate vectorial representations of visual classification tasks that can be used to reason about the nature of those tasks and their relations. Given a dataset with ground-truth labels and a loss function, we process images through a “probe network” and compute an embedding based on estimates of the Fisher information matrix associated with the probe network parameters. This provides

Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games. We build on this previous work by applying RL algorithms to a selection of canonical online stochastic optimization problems with a range of practical applications: Bin Packing, Newsvendor, and Vehicle Routing. While there is a nascent literature that applies RL to these problems, there are no commonly

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

We propose a novel neural topic model in the Wasserstein autoencoders (WAE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much

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

MLIO is a high performance data access library for machine learning tasks with support for multiple data formats. It makes it easy for scientists to train models on their data without worrying about the format or where it's stored. Algorithm developers can also use MLIO to build production-quality algorithms that support a rich variety of data formats and provide helpful parsing and validation messages

In this paper we study techniques to improve the performance of bilinear embedding methods for knowledge graph completion on large datasets, where at each epoch the model sees a very small percentage of the training data, and the number of generated negative examples for each positive example is limited to a small portion of the entire set of entities. We first present a heuristic method to infer the types

We introduce Gluon Time Series (GluonTS)1, a library for deep-learning-based time series modeling. GluonTS simplifies the development of and experimentation with time series models for common tasks such as forecasting or anomaly detection. It provides all necessary components and tools that scientists need for quickly building new models, for efficiently running and analyzing experiments and for evaluating

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

This is a minimal implementation of the HTTP/1.0 and HTTP/1.1 protocols. This HTTP implementation is stateless thus it does not support chunking or compression. The micro-http implementation is used in production by Firecracker. As micro-http uses std::os::unix this crates only supports Unix-like targets.

Building socialbots that can have deep, engaging open-domain conversations with humans is one of the grand challenges of artificial intelligence (AI). To this end, bots need to be able to leverage world knowledge spanning several domains effectively when conversing with humans who have their own world knowledge. Existing knowledge-grounded conversation datasets are primarily stylized with explicit roles

Pre-trained models have demonstrated their effectiveness in many downstream natural language processing (NLP) tasks. The availability of multilingual pre-trained models enables zero-shot transfer of NLP tasks from high resource languages to low resource ones. However, recent research in improving pre-trained models focuses heavily on English. While it is possible to train the latest neural architectures