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Continual Learning Dialogue Systems - Learning during Conversation

Published: 07 July 2022 Publication History

Abstract

Dialogue systems, commonly known as Chatbots, have gained escalating popularity in recent years due to their wide-spread applications in carrying out chit-chat conversations with users and accomplishing various tasks as personal assistants. However, they still have some major weaknesses. One key weakness is that they are typically trained from pre-collected and manually-labeled data and/or written with handcrafted rules. Their knowledge bases (KBs) are also fixed and pre-compiled by human experts. Due to the huge amount of manual effort involved, they are difficult to scale and also tend to produce many errors ought to their limited ability to understand natural language and the limited knowledge in their KBs. Thus, when these systems are deployed, the level of user satisfactory is often low.
In this tutorial, we introduce and discuss methods to give chatbots the ability to continuously and interactively learn new knowledge during conversation, i.e. "on-the-job" by themselves so that as the systems chat more and more with users, they become more and more knowledgeable and improve their performance over time. The first half of the tutorial focuses on introducing the paradigm of lifelong and continual learning and discuss various related problems and challenges in conversational AI applications. In the second half, we present recent advancements on the topic, with a focus on continuous lexical and factual knowledge learning in dialogues, open-domain dialogue learning after deployment and learning of new language expressions via user interactions for language grounding applications (e.g. natural language interfaces). Finally, we conclude with a discussion on the scopes for continual conversational skill learning and present some open challenges for future research.

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  • (2023)Machine learning from casual conversationMachine Learning10.1007/s10994-023-06383-0112:12(4789-4836)Online publication date: 27-Oct-2023
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cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2022

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Author Tags

  1. conversational ai
  2. conversational ir
  3. dialogue and interactive systems
  4. learning after deployment
  5. lifelong and continual learning

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Cited By

View all
  • (2024)RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly RepresentationsDatabase Systems for Advanced Applications10.1007/978-981-97-5569-1_7(104-119)Online publication date: 13-Dec-2024
  • (2024)TiNID: A Transfer and Interpretable LLM-Enhanced Framework for New Intent DiscoveryMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70362-1_12(195-212)Online publication date: 22-Aug-2024
  • (2023)Machine learning from casual conversationMachine Learning10.1007/s10994-023-06383-0112:12(4789-4836)Online publication date: 27-Oct-2023
  • (2023)Polizeiliche Stabsarbeit – Erfordernisse für die ZukunftHandbuch Polizeimanagement10.1007/978-3-658-34388-0_103(739-761)Online publication date: 12-Mar-2023
  • (2022)A Review of Plan-Based Approaches for Dialogue ManagementCognitive Computation10.1007/s12559-022-09996-014:3(1019-1038)Online publication date: 31-Jan-2022
  • (2022)Polizeiliche Stabsarbeit – Erfordernisse für die ZukunftHandbuch Polizeimanagement10.1007/978-3-658-34394-1_103-1(1-23)Online publication date: 26-Mar-2022

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