NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation
arXiv:2103.02548 · doi:10.1609/aaai.v35i16.17649
Abstract
In this paper, we propose a Chinese multi-turn topic-driven conversation dataset, NaturalConv, which allows the participants to chat anything they want as long as any element from the topic is mentioned and the topic shift is smooth. Our corpus contains 19.9K conversations from six domains, and 400K utterances with an average turn number of 20.1. These conversations contain in-depth discussions on related topics or widely natural transition between multiple topics. We believe either way is normal for human conversation. To facilitate the research on this corpus, we provide results of several benchmark models. Comparative results show that for this dataset, our current models are not able to provide significant improvement by introducing background knowledge/topic. Therefore, the proposed dataset should be a good benchmark for further research to evaluate the validity and naturalness of multi-turn conversation systems. Our dataset is available at https://ailab.tencent.com/ailab/nlp/dialogue/#datasets.
Accepted as a main track paper at AAAI 2021
References in corpus (9)
- BERTScore: Evaluating Text Generation with BERT
- Wizard of Wikipedia: Knowledge-Powered Conversational agents
- Neural Approaches to Conversational AI
- Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
- An Interpretable Reasoning Network for Multi-Relation Question Answering
- Flexible End-to-End Dialogue System for Knowledge Grounded Conversation
- Proactive Human-Machine Conversation with Explicit Conversation Goals
- Towards Exploiting Background Knowledge for Building Conversation Systems
- KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation