# How to Improve User Experience (and Behavior): Three Papers from Stanford's Alexa Prize Team

DevFeed: [How to Improve User Experience (and Behavior): Three Papers from Stanford's Alexa Prize Team](<https://devfeed.tech/articles/how-to-improve-user-experience-and-behavior-three-papers-from-stanford-s-alexa-prize-team-7578.md>)

Original publisher: [Read original article](<https://ai.stanford.edu/blog/alexa-sigdial/>)

Author: A Href; Amelia Hardy; Haojun Li; Abigail See

Published: 2022-02-01T08:00:00Z

Content type: article

Language: en

Sources: [The Stanford AI Lab Blog](<https://devfeed.tech/sources/the-stanford-ai-lab-blog.md>)

Topics: [User Experience](<https://devfeed.tech/topics/user-experience.md>), [Bot](<https://devfeed.tech/topics/bot.md>), [Chat Bot](<https://devfeed.tech/topics/chatbot.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [alexa](<https://devfeed.tech/tags/alexa.md>), [chat](<https://devfeed.tech/tags/chat.md>), [code](<https://devfeed.tech/tags/code.md>), [developers](<https://devfeed.tech/tags/developers.md>), [experience](<https://devfeed.tech/tags/experience.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [modular](<https://devfeed.tech/tags/modular.md>), [neural](<https://devfeed.tech/tags/neural.md>), [scripted](<https://devfeed.tech/tags/scripted.md>)

## AI overview

This article presents research from Stanford's Alexa Prize team on improving user experience and behavior in socialbot conversations. It discusses user dissatisfaction, responses to offensive behavior, and how conversational control can be balanced between users and bots. The Chirpy Cardinal socialbot uses a modular combination of neural generation and scripted dialogue to support broad, open-domain conversations.

## Source excerpt

Introduction In 2019, Stanford entered the Alexa Prize Socialbot Grand Challenge 3 for the first time, with its bot Chirpy Cardinal, which went on to win 2nd place in the competition. In our previous post, we discussed the technical structure of our socialbot and how developers can use our open-source code to develop their own. In this post we share further research conducted while developing Chirpy Cardinal to discover common pain points that users encounter when interacting with socialbots, and strategies for addressing them. The Alexa Prize is a unique research setting, as it allows researchers to study how users interact with a bot when doing so solely for their own motivations. During the competition, US-based Alexa users can say the phrase "let's chat" to speak in English to an anonymous and randomly-selected competing bot. They are free to end the conversation at any time. Since Alexa Prize socialbots are intended to create as natural an experience as possible, they should be capable of long, open-domain social conversations with high coverage of topics. We observed that Chirpy users were interested in many different subjects, from current events (e.g., the coronavirus) to pop culture (e.g., the movie Frozen 2) to personal interests (e.g,. their pets). Chirpy achieves its coverage of these diverse topics by using a modular design that combines both neural generation and scripted dialogue, as described in our previous post. We used this setting to study three questions about socialbot conversations: What do users complain about, and how can we learn from the complaints to improve neurally generated dialogue? What strategies are effective and ineffective in handling and deterring offensive user behavior? How can we shift the balance of power, such that both users and the bot are meaningfully controlling the conversation? We've published papers on each of these topics at SIGDIAL 2021 and in this post, we'll share key findings which provide practical insights for