Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition

• URL: http://arxiv.org/abs/2205.14748v1
• Date: Sun, 29 May 2022 19:42:25 GMT
• Title: Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition
• Authors: Pengshan Cai, Hui Wan, Fei Liu, Mo Yu, Hong Yu, Sachindra Joshi
• Abstract summary: We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. Our information-acquisition-oriented dialogue system employs a novel adaptation of reinforced self-play so that the system can be transferred to various domains without in-domain dialogue data.
• Score: 30.91417206129677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. Our information-acquisition-oriented dialogue system employs a novel adaptation of reinforced self-play so that the system can be transferred to various domains without in-domain dialogue data, and can carry out conversations both informative and attentive to users. Our extensive subjective and objective evaluations on three large public data corpora demonstrate the effectiveness of our system to deliver knowledge-intensive and attentive conversations and help end users substantially gain knowledge without reading passages. Our code and datasets are publicly available for follow-up research.

Related papers

• PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model [79.64376762489164]
  PK-Chat is a Pointer network guided generative dialogue model, incorporating a unified pretrained language model and a pointer network over knowledge graphs. The words generated by PK-Chat in the dialogue are derived from the prediction of word lists and the direct prediction of the external knowledge graph knowledge. Based on the PK-Chat, a dialogue system is built for academic scenarios in the case of geosciences.
  arXiv  Detail & Related papers  (2023-04-02T18:23:13Z)
• Towards leveraging latent knowledge and Dialogue context for real-world conversational question answering [1.0152838128195467]
  We propose to leverage latent knowledge in existing conversation logs via a neural Retrieval-Reading system. Our experiments show that our Retrieval-Reading system can exploit retrieved background knowledge to generate significantly better answers.
  arXiv  Detail & Related papers  (2022-12-17T20:36:17Z)
• Lifelong and Continual Learning Dialogue Systems [14.965054800464259]
  Book introduces the new paradigm of lifelong learning dialogue systems. As the systems chat more and more with users or learn more from external sources, they become more knowledgeable and better at conversing.
  arXiv  Detail & Related papers  (2022-11-12T02:39:41Z)
• 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)
• 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)
• End-to-end Spoken Conversational Question Answering: Task, Dataset and Model [92.18621726802726]
  In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. We propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows. Our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering.
  arXiv  Detail & Related papers  (2022-04-29T17:56:59Z)
• Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph [0.9236074230806579]
  We present the third version of the open-domain dialogue system Alquist developed within the Alexa Prize 2020 competition. The main novel contribution is the introduction of a system based on a conversational knowledge graph and adjacency pairs. We discuss and describe Alquist's pipeline, data acquisition and processing, dialogue manager, NLG, knowledge aggregation, and a hierarchy of adjacency pairs.
  arXiv  Detail & Related papers  (2020-11-06T10:10:02Z)
• Towards Data Distillation for End-to-end Spoken Conversational Question Answering [65.124088336738]
  We propose a new Spoken Conversational Question Answering task (SCQA) SCQA aims at enabling QA systems to model complex dialogues flow given the speech utterances and text corpora. Our main objective is to build a QA system to deal with conversational questions both in spoken and text forms.
  arXiv  Detail & Related papers  (2020-10-18T05:53:39Z)
• Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity [10.409312809724458]
  We design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know. We collect and release 14K dialogs (181K utterances) where users and assistants converse about geographic topics. This dataset is annotated with pre-existing user knowledge, message-level dialog acts, grounding to Wikipedia, and user reactions to messages.
  arXiv  Detail & Related papers  (2020-05-01T01:55:09Z)
• Attention over Parameters for Dialogue Systems [69.48852519856331]
  We learn a dialogue system that independently parameterizes different dialogue skills, and learns to select and combine each of them through Attention over Parameters (AoP) The experimental results show that this approach achieves competitive performance on a combined dataset of MultiWOZ, In-Car Assistant, and Persona-Chat.
  arXiv  Detail & Related papers  (2020-01-07T03:10:42Z)

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.