# Alexa, Let's Work Together: Introducing the First Alexa Prize TaskBot Challenge on Conversational Task Assistance

arXiv: https://arxiv.org/abs/2209.06321 · HTML: https://mlsanity.com/paper/2209.06321/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [UnifiedQA: Crossing Format Boundaries With a Single QA System](https://mlsanity.com/paper/2005.00700/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Conversational Information Seeking](https://mlsanity.com/paper/2201.08808/) (2022)
- [Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize](https://mlsanity.com/paper/1812.10757/) (2018)
- [MIMICS: A Large-Scale Data Collection for Search Clarification](https://mlsanity.com/paper/2006.10174/) (2020)
- [Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding](https://mlsanity.com/paper/1711.00549/) (2017)

## Built on by (most cited first, 15 of 15)

- [Evaluating Human-Language Model Interaction](https://mlsanity.com/paper/2212.09746/) (2022)
- [Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs](https://mlsanity.com/paper/2303.08941/) (2023)
- [Plan-Grounded Large Language Models for Dual Goal Conversational Settings](https://mlsanity.com/paper/2402.01053/) (2024)
- [SpeakRL: Synergizing Reasoning, Speaking, and Acting in Language Models with Reinforcement Learning](https://mlsanity.com/paper/2512.13159/) (2025)
- [HierTOD: A Task-Oriented Dialogue System Driven by Hierarchical Goals](https://mlsanity.com/paper/2411.07152/) (2024)
- [Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System](https://mlsanity.com/paper/2307.16081/) (2023)
- [Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI](https://mlsanity.com/paper/2308.05221/) (2023)
- [Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants](https://mlsanity.com/paper/2410.12891/) (2024)
- [The Wizard of Curiosities: Enriching Dialogues with Fun Facts](https://mlsanity.com/paper/2309.11283/) (2023)
- [TWIZ-v2: The Wizard of Multimodal Conversational-Stimulus](https://mlsanity.com/paper/2310.02118/) (2023)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features](https://mlsanity.com/paper/2309.11307/) (2023)
- [Grounded Complex Task Segmentation for Conversational Assistants](https://mlsanity.com/paper/2309.11271/) (2023)
- [Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model](https://mlsanity.com/paper/2310.13106/) (2023)
- [Collaborative and Proactive Management of Task-Oriented Conversations](https://mlsanity.com/paper/2510.05110/) (2025)

## Abstract (arXiv)

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 agents attempt to assist users with increasingly complex tasks, new conversational AI techniques and evaluation platforms are needed. The Alexa Prize TaskBot challenge, established in 2021, builds on the success of the SocialBot challenge by introducing the requirements of interactively assisting humans with real-world Cooking and Do-It-Yourself tasks, while making use of both voice and visual modalities. This challenge requires the TaskBots to identify and understand the user's need, identify and integrate task and domain knowledge into the interaction, and develop new ways of engaging the user without distracting them from the task at hand, among other challenges. This paper provides an overview of the TaskBot challenge, describes the infrastructure support provided to the teams with the CoBot Toolkit, and summarizes the approaches the participating teams took to overcome the research challenges. Finally, it analyzes the performance of the competing TaskBots during the first year of the competition.