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Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-world QA applications, multiple answer scenarios arise where consolidating answers into a comprehensive and non-redundant set of answers is a more efficient user interface. In this paper, we formulate the problem of answer consolidation

Project Description This is a test framework for the bias bounties project. Getting Started as a Bounty Hunter If you are interacting with this codebase as a "bounty hunter", you'll need to have a way to run Jupyter notebooks. The easiest way to do this is to download Anaconda, which will also manage all of your python packages for you. See here for installation instructions: https://docs.anaconda.com/anaconda

This module implements a set of functions to perform MAXENT from a causal perspective. The code here can be used to reproduce the results in the publication Obtaining Causal Information by Merging Datasets with MAXENT. The parts of the plots using KCI are, unfortunately, not available. To reproduce the results in the article create a python 3.6+ environment, pip install all the requirements.txt file and

As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased (Freitag et al., 2021; Isabelle et al., 2017). In particular, gender accuracy in translation (Choubey et al., 2021; Saunders and Byrne, 2020) can have implications in terms of output fluency, translation accuracy, and ethics. In this paper, we introduce MTGenEval

Robots operating in human spaces must be able to engage in natural language interaction, both understanding and executing instructions, and using conversation to resolve ambiguity and correct mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human–human, interactive dialogues to complete household tasks in simulation. A Commander with access to oracle information about a task communicates

Skill Components are “ready to use” experiences that you can easily add to your skills, and configure them according to your needs. Each component is a collection of skill primitives, such as VUI dialogs & intents, Alexa Presentation Language (APL) documents, skill code, skill connection tasks, and skill events. They bring together design best practices and pre-built voice experiences, which solve for a

Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students to explore and compete to develop conversational agents through the SocialBot Grand Challenge. The goal of the challenge is to build agents capable of conversing coherently and engagingly with humans on popular topics for 20 minutes, while achieving an average rating of at least 4.0/5.0. However, as conversational

We describe QuakerBot, a dialog system that helps users with household tasks and a participant in the Alexa Prize TaskBot Challenge. QuakerBot can process a variety of user requests, search for instructions from web resources such as wikiHow or Whole Foods Market recipes, answer related questions, and so on. Its components simultaneously consist of large language models with an impressive few-shot performance

We present GRILLBot, a multi-modal task-oriented voice assistant to guide users through complex real-world tasks for the Alexa TaskBot Challenge. An effective TaskBot has to guide a user through a long and complex task, be engaging, and help solve problems along the way. GRILLBot achieves this in the domains of cooking and home improvement by helping search over a large task corpus with mixed- initiative

As people learn to interact with AI assistants such as Alexa, their needs change and become more complex. Alexa must evolve accordingly, and offer more sophisticated experiences, which require addressing increasingly complex AI research challenges. In the last few years, Amazon has offered university teams a chance to partner with Amazon scientists in order push the boundaries of the state of the art in

This paper describes the vision, scientific contributions, and technical details of the Task Wizard (TWIZ) team’s participation in the Alexa TaskBot Challenge 2021. Our bot design envisions the support of an engaging experience, where users are guided through multimodal conversations, towards the successful completion of the selected task. This is achieved through four key principles: a) robust dialog interaction

Conversational Artificial Intelligence (AI) has been a long-standing area of exploration in the research community and has now penetrated both academia and industries with products such as Siri and Alexa. In this work, we present COoking-aNd-DIy-TAsk-based (Condita) ChatBot, a task-oriented dialogue system, for the 2021 Alexa Prize TaskBot Challenge. Condita provides an engaging multi-modal agent that assists

We present Maruna Bot, a Task-Oriented Dialogue System (TODS) that assists people in cooking or Do-It-Yourself (DIY) tasks using either a speech-only or multimodal (speech and screen) interface. Building such a system is challenging, because it touches many research areas including language understanding, text generation, task planning, dialogue state tracking, question answering, multi-modal retrieval,

In this paper, we introduce Gauchobot, a task-oriented dialogue system developed for the Alexa Prize TaskBot Challenge. We identified two main obstacles to building better conversational AI assistants in real-world applications. The first is great human efforts needed in data annotation and engineering a dialogue system that provides service in a new domain from scratch. The high cost, often ignored by

Tartan is a multi-domain task-oriented bot that assists users with two different tasks: 1. cooking with recipes from Whole Foods and 2. doing projects from WikiHow. The bot’s system is divided into two stages. In the first stage, the bot assists the user in identifying a task that they want to do. In the second stage, the bot guides the users through sequences of step-by-step instructions. We developed

In this paper, we present Howdy Y’all, a multi-modal task-oriented dialogue agent developed for the 2021-2022 Alexa Prize TaskBot competition. Our design principles guiding Howdy Y’all aim for high user satisfaction through friendly and trustworthy encounters, minimization of negative conversation edge cases, and wide coverage over many tasks. Hence, Howdy Y’all is built upon a rapid prototyping platform

This paper introduces Miutsu, National Taiwan University’s Alexa Prize TaskBot, which is designed to assist users in completing tasks requiring multiple steps and decisions in two different domains – home improvement and cooking. We overview our system design and architectural goals, and detail the proposed core elements, including question answering, task retrieval, social chatting, and various conversational

We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks. TacoBot is designed with a user-centered principle and aspires to deliver a collaborative and accessible dialogue experience. Towards that end, it is equipped with accurate language understanding, flexible dialogue management

New features go beyond conventional effect estimation by attributing events to individual components of complex systems.