CASPR: A Commonsense Reasoning-based Conversational Socialbot

• URL: http://arxiv.org/abs/2110.05387v1
• Date: Mon, 11 Oct 2021 16:23:24 GMT
• Title: CASPR: A Commonsense Reasoning-based Conversational Socialbot
• Authors: Kinjal Basu, Huaduo Wang, Nancy Dominguez, Xiangci Li, Fang Li, Sarat Chandra Varanasi, Gopal Gupta
• Abstract summary: We report on the design and development of the CASPR system, a socialbot designed to compete in the Amazon Alexa Socialbot Challenge 4. CASPR's distinguishing characteristic is that it will use automated commonsense reasoning to truly "understand" dialogs. Three main requirements of a socialbot are that it should be able to "understand" users' utterances, possess a strategy for holding a conversation, and be able to learn new knowledge.
• Score: 8.652993697080149
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We report on the design and development of the CASPR system, a socialbot designed to compete in the Amazon Alexa Socialbot Challenge 4. CASPR's distinguishing characteristic is that it will use automated commonsense reasoning to truly "understand" dialogs, allowing it to converse like a human. Three main requirements of a socialbot are that it should be able to "understand" users' utterances, possess a strategy for holding a conversation, and be able to learn new knowledge. We developed techniques such as conversational knowledge template (CKT) to approximate commonsense reasoning needed to hold a conversation on specific topics. We present the philosophy behind CASPR's design as well as details of its implementation. We also report on CASPR's performance as well as discuss lessons learned.

Related papers

• A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP [3.17686396799427]
  We propose AutoCompanion, a socialbot that uses an LLM model to translate natural language into predicates. This paper presents the framework design and how an LLM is used to parse user messages and generate a response from the s(CASP) engine output.
  arXiv  Detail & Related papers  (2024-07-26T04:13:43Z)
• Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs [4.212937192948915]
  Large Language Models (LLMs) rely on pattern-matching rather than a true understanding of the semantic meaning of a sentence. To generate an assuredly correct response, one has to "understand" the semantics of a sentence. We describe the AutoConcierge system that leverages ASP to develop a conversational agent that can truly "understand" human dialogs.
  arXiv  Detail & Related papers  (2023-03-15T21:10:33Z)
• Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent [65.68144111226626]
  Chirpy Cardinal aims to be both informative and conversational. We let both the user and bot take turns driving the conversation. Chirpy Cardinal placed second out of nine bots in the Alexa Prize Socialbot Grand Challenge.
  arXiv  Detail & Related papers  (2022-07-25T09:57:23Z)
• ProsocialDialog: A Prosocial Backbone for Conversational Agents [104.92776607564583]
  We introduce ProsocialDialog, the first large-scale dialogue dataset to teach conversational agents to respond to problematic content following social norms. Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K RoTs, and 497K dialogue safety labels. With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost.
  arXiv  Detail & Related papers  (2022-05-25T11:48:47Z)
• KETOD: Knowledge-Enriched Task-Oriented Dialogue [77.59814785157877]
  Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. We investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.
  arXiv  Detail & Related papers  (2022-05-11T16:01:03Z)
• An Approach to Inference-Driven Dialogue Management within a Social Chatbot [10.760026478889667]
  Instead of framing conversation as a sequence of response generation tasks, we model conversation as a collaborative inference process. Our pipeline accomplishes this modelling in three broad stages. This approach lends itself to understanding latent semantics of user inputs, flexible initiative taking, and responses that are novel and coherent with the dialogue context.
  arXiv  Detail & Related papers  (2021-10-31T19:01:07Z)
• Intelligent Conversational Android ERICA Applied to Attentive Listening and Job Interview [41.789773897391605]
  We have developed an intelligent conversational android ERICA. We set up several social interaction tasks for ERICA, including attentive listening, job interview, and speed dating. It has been evaluated with 40 senior people, engaged in conversation of 5-7 minutes without a conversation breakdown.
  arXiv  Detail & Related papers  (2021-05-02T06:37:23Z)
• Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention [55.77218465471519]
  This paper proposes an innovative framework to train chatbots to possess human-like intentions. Our framework included a guiding robot and an interlocutor model that plays the role of humans. We examined our framework using three experimental setups and evaluate the guiding robot with four different metrics to demonstrated flexibility and performance advantages.
  arXiv  Detail & Related papers  (2021-03-30T15:24:37Z)
• CASS: Towards Building a Social-Support Chatbot for Online Health Community [67.45813419121603]
  The CASS architecture is based on advanced neural network algorithms. It can handle new inputs from users and generate a variety of responses to them. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support.
  arXiv  Detail & Related papers  (2021-01-04T05:52:03Z)
• Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents [60.27066549589362]
  Social language used by human agents is associated with greater users' responsiveness and task completion. The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element. Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.
  arXiv  Detail & Related papers  (2020-12-29T08:22:48Z)
• Building A User-Centric and Content-Driven Socialbot [2.072266782237039]
  We develop a system architecture that is capable of accommodating dialog strategies that we designed for socialbot conversations. The architecture consists of a multi-dimensional language understanding module for analyzing user utterances. We construct a new knowledge base to power the socialbot by collecting social chat content from a variety of sources.
  arXiv  Detail & Related papers  (2020-05-06T07:11:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.