Alexa Prize Proceedings

Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students and faculty to explore and compete in the development of conversational agents through the SocialBot Grand Challenge, whose goal is to build agents capable of conversing coherently and engagingly with humans on popular topics. As conversational agents attempt to assist users with increasingly complex tasks,

In the inaugural Alexa Prize TaskBot Challenge, we introduced TACO 1.0, a task-oriented digital assistant, designed to guide users through multi-step tasks in cooking and home improvement. Equipped with a suite of components including language understanding, dialogue management, and response generation, bolstered by a search engine, TACO 1.0 set a robust foundation in user-centered assistance. Building

As conversational agents become sophisticated, they are becoming more prevalent in the daily lives of people. They have the ability to help users accomplish daily tasks and help in their day-to-day lives. In this report we summarize our findings and development of our taskbot: Do it With Bot (DiWBot) built to help people in cooking and DIY tasks for the 2023 Alexa Prize TaskBot Challenge. We present the

EvoquerBot, developed for the TaskBot challenge, is a multimedia chatbot designed to assist users in completing cooking and DIY tasks within a single session. The bot leverages a coordinated orchestration of submodules for intent classification, task recommendation, task description, and step navigation. This paper addresses the challenges of short development and model training time, data quality in both

This paper presents Sage, a task-oriented multimodal conversational agent devel- oped for the Alexa Prize TaskBot Challenge 2. Focusing on cooking and DIY tasks, Sage integrates task-oriented dialogues with engaging general chats for a human-like interaction model. Its innovative hierarchical dialogue state management, based on hierarchical state machines, enables a flexible conversation flow managing both

In this report, we describe the vision, challenges, and scientific contributions of the Task Wizard team, TWIZ, in the Alexa Prize TaskBot Challenge 2022. Our vision, is to build TWIZ bot as an helpful, multimodal, knowledgeable, and engaging assistant that can guide users towards the successful completion of complex manual tasks. To achieve this, we focus our efforts on three main research questions: (

In the rapidly evolving landscape of multimodal interactive technologies, a critical gap persists in their utility and reach across diverse user populations. These technologies, while advanced, exhibit a narrow application of modalities, consequently marginalizing certain groups. Additionally, their superficial representations of context pose a challenge in accommodating users with varied preferences, cultural

This paper outlines the design and deployment of BoilerBot: a task-oriented multi- modal conversational agent developed for the Alexa Prize TaskBot 2 competition. BoilerBot features flexible response generation, leveraging large language models (LLMs) to enable adaptable user experiences. We discuss our novel contributions towards advancing state-of-the-art task-oriented conversational agents, highlighting

We present our Alexa TaskBot Challenge virtual assistant, GRILLBot-v2, which advances multi-modal task-oriented conversations by leveraging generative lan- guage models and automatic extraction and augmentation of interactive task data. GRILLBot-v2 is a conversational system based on open-source software and pub- licly available data. The task of manually crafting engaging conversational content is expensive

We introduce MarunaBot V2, an advanced Task-Oriented Dialogue System (TODS) primarily aimed at aiding users in cooking and Do-It-Yourself tasks. We utilized large language models (LLMs) for data generation and inference, and implemented hybrid methods for intent classification, retrieval, and question answering, striking a balance between efficiency and performance. A key feature of our system is its multi-modal