# Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize

arXiv: https://arxiv.org/abs/2303.04953 · HTML: https://mlsanity.com/paper/2303.04953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 62 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 25 of 25)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](https://mlsanity.com/paper/1506.06714/) (2015)
- [A Deep Reinforcement Learning Chatbot](https://mlsanity.com/paper/1709.02349/) (2017)
- [Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize](https://mlsanity.com/paper/1812.10757/) (2018)
- [Emora: An Inquisitive Social Chatbot Who Cares For You](https://mlsanity.com/paper/2009.04617/) (2020)
- [Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems](https://mlsanity.com/paper/1610.07149/) (2016)
- [Sounding Board: A User-Centric and Content-Driven Social Chatbot](https://mlsanity.com/paper/1804.10202/) (2018)
- [Gunrock 2.0: A User Adaptive Social Conversational System](https://mlsanity.com/paper/2011.08906/) (2020)
- [Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization](https://mlsanity.com/paper/2109.07968/) (2021)
- [Athena: Constructing Dialogues Dynamically with Discourse Constraints](https://mlsanity.com/paper/2011.10683/) (2020)
- [CASPR: A Commonsense Reasoning-based Conversational Socialbot](https://mlsanity.com/paper/2110.05387/) (2021)
- [Entertaining and Opinionated but Too Controlling: A Large-Scale User Study of an Open Domain Alexa Prize System](https://mlsanity.com/paper/1908.04832/) (2019)
- [Proto: A Neural Cocktail for Generating Appealing Conversations](https://mlsanity.com/paper/2109.02513/) (2021)
- [Modeling Performance in Open-Domain Dialogue with PARADISE](https://mlsanity.com/paper/2110.11164/) (2021)
- [Athena 2.0: Contextualized Dialogue Management for an Alexa Prize SocialBot](https://mlsanity.com/paper/2111.02519/) (2021)
- [Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue](https://mlsanity.com/paper/1709.05411/) (2017)
- [Avoiding Echo-Responses in a Retrieval-Based Conversation System](https://mlsanity.com/paper/1712.05626/) (2017)
- [Audrey: A Personalized Open-Domain Conversational Bot](https://mlsanity.com/paper/2011.05910/) (2020)
- [An Approach to Inference-Driven Dialogue Management within a Social Chatbot](https://mlsanity.com/paper/2111.00570/) (2021)

## Abstract (arXiv)

There has been an increased focus on creating conversational open-domain dialogue systems in the spoken dialogue community. Unlike traditional dialogue systems, these conversational systems cannot assume any specific information need or domain restrictions, i.e., the only inherent goal is to converse with the user on an unknown set of topics. While massive improvements in Natural Language Understanding (NLU) and the growth of available knowledge resources can partially support a robust conversation, these conversations generally lack the rapport between two humans that know each other. We developed a robust open-domain conversational system, Athena, that real Amazon Echo users access and evaluate at scale in the context of the Alexa Prize competition. We experiment with methods intended to increase intimacy between Athena and the user by heuristically developing a rule-based user model that personalizes both the current and subsequent conversations and evaluating specific personal opinion question strategies in A/B studies. Our results show a statistically significant positive impact on perceived conversation quality and length when employing these strategies.