A Scalable Chatbot Platform Leveraging Online Community Posts: A Proof-of-Concept Study

• URL: http://arxiv.org/abs/2001.03278v1
• Date: Fri, 10 Jan 2020 01:45:45 GMT
• Title: A Scalable Chatbot Platform Leveraging Online Community Posts: A Proof-of-Concept Study
• Authors: Sihyeon Jo, Seungryong Yoo, Sangwon Im, Seung Hee Yang, Tong Zuo, Hee-Eun Kim, SangWook Han, Seong-Woo Kim
• Abstract summary: We verify the feasibility of constructing a data-driven chatbots with processed online community posts by using them as pseudo-conversational data. We argue that chatbots for various purposes can be built extensively through the pipeline exploiting the common structure of community posts.
• Score: 4.623392924486831
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of natural language processing algorithms and the explosive growth of conversational data are encouraging researches on the human-computer conversation. Still, getting qualified conversational data on a large scale is difficult and expensive. In this paper, we verify the feasibility of constructing a data-driven chatbot with processed online community posts by using them as pseudo-conversational data. We argue that chatbots for various purposes can be built extensively through the pipeline exploiting the common structure of community posts. Our experiment demonstrates that chatbots created along the pipeline can yield the proper responses.

Related papers

• Computational Argumentation-based Chatbots: a Survey [0.4024850952459757]
  The present survey sifts through the literature to review papers concerning this kind of argumentation-based bot. It draws conclusions about the drawbacks and benefits of this approach. It also envisaging possible future development and integration with the Transformer-based architecture and state-of-the-art Large Language models.
  arXiv  Detail & Related papers  (2024-01-07T11:20:42Z)
• ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models [125.7209927536255]
  We propose ChatCoT, a tool-augmented chain-of-thought reasoning framework for chat-based LLMs. In ChatCoT, we model the chain-of-thought (CoT) reasoning as multi-turn conversations, to utilize tools in a more natural way through chatting. Our approach can effectively leverage the multi-turn conversation ability of chat-based LLMs, and integrate the thought chain following and tools manipulation in a unified way.
  arXiv  Detail & Related papers  (2023-05-23T17:54:33Z)
• PLACES: Prompting Language Models for Social Conversation Synthesis [103.94325597273316]
  We use a small set of expert-written conversations as in-context examples to synthesize a social conversation dataset using prompting. We perform several thorough evaluations of our synthetic conversations compared to human-collected conversations.
  arXiv  Detail & Related papers  (2023-02-07T05:48:16Z)
• Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data [15.808841433843742]
  Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. We explore what design factors of prompts can help steer chatbots to talk naturally and collect data reliably.
  arXiv  Detail & Related papers  (2023-01-14T07:29:36Z)
• Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks [76.11480953550013]
  We take a step towards automatically generating conversational data using Generative Conversational Networks. We evaluate our approach on conversations with and without knowledge on the Topical Chat dataset.
  arXiv  Detail & Related papers  (2022-07-22T22:37:14Z)
• Training Conversational Agents with Generative Conversational Networks [74.9941330874663]
  We use Generative Conversational Networks to automatically generate data and train social conversational agents. We evaluate our approach on TopicalChat with automatic metrics and human evaluators, showing that with 10% of seed data it performs close to the baseline that uses 100% of the data.
  arXiv  Detail & Related papers  (2021-10-15T21:46:39Z)
• Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency [28.255324166852535]
  We propose the Addressing Inquiries about History (AIH) framework for the consistency evaluation. At the conversation stage, AIH attempts to address appropriate inquiries about the dialogue history to induce the chatbots to redeclare the historical facts or opinions. At the contradiction recognition stage, we can either employ human judges or a natural language inference (NLI) model to recognize whether the answers to the inquiries are contradictory with history.
  arXiv  Detail & Related papers  (2021-06-04T03:04:13Z)
• 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)
• Conversations with Search Engines: SERP-based Conversational Response Generation [77.1381159789032]
  We create a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines. We also develop a state-of-the-art pipeline for conversations with search engines, the Conversations with Search Engines (CaSE) using this dataset. CaSE enhances the state-of-the-art by introducing a supporting token identification module and aprior-aware pointer generator.
  arXiv  Detail & Related papers  (2020-04-29T13:07:53Z)
• If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills [4.395837214164745]
  It is challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions. We are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots.
  arXiv  Detail & Related papers  (2020-02-05T16:52:52Z)

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.