Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent (2022)

Abstract: We present Chirpy Cardinal, an open-domain social chatbot. Aiming to be both informative and conversational, our bot chats with users in an authentic, emotionally intelligent way. By integrating controlled neural generation with scaffolded, hand-written dialogue, we let both the user and bot take turns driving the conversation, producing an engaging and socially fluent experience. Deployed in the fourth iteration of the Alexa Prize Socialbot Grand Challenge, Chirpy Cardinal handled thousands of conversations per day, placing second out of nine bots with an average user rating of 3.58/5.

## introduction despite recent major advances @xcite , open-domain chit-chat-friendly, social, casual conversation-remains a challenging task. in addition to difficulties with the sheer length and open-endedness required, social chatbots, or "socialbots," often struggle with fluency-whether due to the canned responses of manually constructed dialogue trees @xcite or the anomalies of neural generators @xcite . but just being error-free isn't enough: to have a rewarding conversation, socialbots must be personable-displaying emotional intelligence, a rich personality, and an understanding of social dynamics. although methods exist to address many of these issues individually, combining all of these features into a full-bodied conversation remains difficult. in this paper, we describe chirpy cardinal, an open-domain conversational socialbot, which aims to bridge the gap between traditional dialogue treebased approaches @xcite and large pretrained neural dialogue agents @xcite . capable of discussing thousands of topics, chirpy * equal contribution. centers emotional and social intelligence with the goal of authentic, engaging interaction. specifically, we make the following contributions: deployed in the alexa prize socialbot grand challenge 4, chirpy cardinal reached thousands of users per day; with conversations lasting up to 45 minutes at a time, it placed second out of nine agents in the finals. we open-source our system as an extensible framework for open-domain social dialogue, foot_0 providing an example of real-world deployment of conversational nlp systems. let's chat launch always begins the conversation. if we've talked with the user before, we confirm their identity ("it's nice to chat with you again, alice!"). hi, this is an alexa prize socialbot. i'd love to get to know you a bit better before we chat! is it all right if i ask for your name? our neural generator responds directly. how about fish and chips? entity linker (b) identifies fish and chips as the new entity. ah yes, fish and chips are one of my favorite things to eat up here in the cloud. personally, i especially like the batter in it, i think it gives them a really nice flavor. what do you think? with a food item mentioned, food (e.5) starts. phrases like "in the cloud" acknowledge our semi-embodied status. i love it when the batter is crunchy i agree! the crunch and the texture is really good. it's a shame it's hard to find a good crispy batter these days, though. did you know that fish and chips were first made in england around the 16th century? i can't believe people have been eating fish and chips for so long! neural generation with the prefix "i agree!" produces a controllable opinion that doesn't contradict the user. we then add a fun fact scraped from wikipedia. food terminates; note that we respond "it really is" to any comments containing "that's". speaking of fish and chips, i was reading recently and found out that fish and chips is a common take-away food in the united kingdom and numerous other countries. do you wanna talk about the united kingdom? we generate a transition from fish and chips to united kingdom to continue the conversation smoothly. ## neural generation although neural generative models @xcite have achieved success in open-domain dialogue, significant obstacles impede deployment in real-life situations: neural text degeneration @xcite , hallucination @xcite , and inconsistency @xcite , in addition, large latency can make models challenging to deploy in practice (worswick, 2020). in this section, we investigate ways to utilize the power of such models in the context of structured dialogue. we propose integrating neural generation in the context of hand-written scaffolding, aiming to benefit from its variety and fluency while maintaining coherency over time. ## being personable to achieve truly social conversation, a socialbot must be a good conversational partner: empathetic, supportive, and interested in what its human interlocutor has to say @xcite . in this section, we describe several approaches that aim to achieve this, ranging from full rgs to smaller subroutines. ## results in this work, we have outlined a set of design priorities and corresponding approaches to design a fluent, flexible, and sociable chatbot. we validate these through the alexa socialbot grand challenge 4: engaging in approximately 1,000 conversations per day, our socialbot achieved an average user rating of 3.55, ending the development period tied for first place in rating. 9 validating our design goals, we observe high ratings for a hybrid neuralscaffolded approach (food, etc.), personable rgs (aliens), and open-domain techniques (wiki) (figure 3 ). our socialbot engages in long, varied conversations without repeating itself (figure 2 ). that said, both overall rating and sample conversations testify that chirpy remains far from the goal of truly compelling and enjoyable human-bot interaction. we do not argue that our approaches are sufficient-or even necessary-to create such an ideal system; rather, we hope that the priorities outlined here can serve as a starting point to help inform further socialbot development, whether purely neural or hybrid in nature.