Talking to myself: self-dialogues as data for conversational agents
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Abstract (arXiv)
Conversational agents are gaining popularity with the increasing ubiquity of smart devices. However, training agents in a data driven manner is challenging due to a lack of suitable corpora. This paper presents a novel method for gathering topical, unstructured conversational data in an efficient way: self-dialogues through crowd-sourcing. Alongside this paper, we include a corpus of 3.6 million words across 23 topics. We argue the utility of the corpus by comparing self-dialogues with standard two-party conversations as well as data from other corpora.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Sep 2018.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1.
5 papers.
- 2021GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injectioncited by 102
- 2022SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generationcited by 34
- 2022SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understandingcited by 28
- 2023UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Promptcited by 3
- 2022Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddingscited by 2
- 2023UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt20 Sep 2023
- 2022Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings27 Oct 2022
- 2022SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation14 Sep 2022
- 2022SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding14 Sep 2022
- 2021GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection29 Nov 2021