Conversation Initiation by Diverse News Contents Introduction

Satoshi Akasaki, Nobuhiro Kaji


Abstract
In our everyday chit-chat, there is a conversation initiator, who proactively casts an initial utterance to start chatting. However, most existing conversation systems cannot play this role. Previous studies on conversation systems assume that the user always initiates conversation, and have placed emphasis on how to respond to the given user’s utterance. As a result, existing conversation systems become passive. Namely they continue waiting until being spoken to by the users. In this paper, we consider the system as a conversation initiator and propose a novel task of generating the initial utterance in open-domain non-task-oriented conversation. Here, in order not to make users bored, it is necessary to generate diverse utterances to initiate conversation without relying on boilerplate utterances like greetings. To this end, we propose to generate initial utterance by summarizing and chatting about news articles, which provide fresh and various contents everyday. To address the lack of the training data for this task, we constructed a novel large-scale dataset through crowd-sourcing. We also analyzed the dataset in detail to examine how humans initiate conversations (the dataset will be released to facilitate future research activities). We present several approaches to conversation initiation including information retrieval based and generation based models. Experimental results showed that the proposed models trained on our dataset performed reasonably well and outperformed baselines that utilize automatically collected training data in both automatic and manual evaluation.
Anthology ID:
N19-1400
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Jill Burstein, Christy Doran, Thamar Solorio
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3988–3998
Language:
URL:
https://aclanthology.org/N19-1400
DOI:
10.18653/v1/N19-1400
Bibkey:
Cite (ACL):
Satoshi Akasaki and Nobuhiro Kaji. 2019. Conversation Initiation by Diverse News Contents Introduction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3988–3998, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Conversation Initiation by Diverse News Contents Introduction (Akasaki & Kaji, NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-1400.pdf