Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching text labels of stickers with previous utterances. However, due to their large quantities, it is impractical to require text labels for the all stickers. Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context history without any external labels. Two main challenges are confronted in this task. One is to learn semantic meaning of stickers without corresponding text labels. Another challenge is to jointly model the candidate sticker with the multi-turn dialog context. To tackle these challenges, we propose a sticker response selector (SRS) model. Specifically, SRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker with each utterance in the dialog history. SRS then learns the short-term and long-term dependency between all interaction results by a fusion network to output the the final matching score. To evaluate our proposed method, we collect a large-scale real-world dialog dataset with stickers from one of the most popular online chatting platform. Extensive experiments conducted on this dataset show that our model achieves the state-of-the-art performance for all commonly-used metrics. Experiments also verify the effectiveness of each component of SRS. To facilitate further research in sticker selection field, we release this dataset of 340K multi-turn dialog and sticker pairs.


翻译:具有生动和感动表达式的粘贴剂在网上短信应用程序中越来越受欢迎,有些作品致力于通过将标签标签标签与先前的语句匹配,自动选择粘贴剂。然而,由于数量庞大,要求所有标签标签都贴上文字标签是不切实际的。因此,在本文中,我们建议根据多转对话框背景历史向用户推荐适当的粘贴剂,而无需任何外部标签。在这项任务中面临两大挑战。一个是学习粘贴剂的语义含义,而没有相应的文本标签。另一个挑战是将候选人粘贴剂与多转对话框环境相匹配。为了应对这些挑战,我们提议采用粘贴贴剂选择(SRS)的文本标签选择(SRS)模式。具体地,SRS首先使用基于粘贴标签的图像编码标签标签标签标签标签标签标签,然后用基于多转动式对话框的自动标签标签标签标签标签,然后用我们所有互动的直径直线和长的直径直径直径直径直径对调的直径直径直径直径直径直径直径直径。

14
下载
关闭预览

相关内容

IFIP TC13 Conference on Human-Computer Interaction是人机交互领域的研究者和实践者展示其工作的重要平台。多年来,这些会议吸引了来自几个国家和文化的研究人员。官网链接:http://interact2019.org/
100+篇《自监督学习(Self-Supervised Learning)》论文最新合集
专知会员服务
159+阅读 · 2020年3月18日
【CMU】机器学习导论课程(Introduction to Machine Learning)
专知会员服务
58+阅读 · 2019年8月26日
Transferring Knowledge across Learning Processes
CreateAMind
24+阅读 · 2019年5月18日
基于PyTorch/TorchText的自然语言处理库
专知
27+阅读 · 2019年4月22日
Call for Participation: Shared Tasks in NLPCC 2019
中国计算机学会
5+阅读 · 2019年3月22日
无监督元学习表示学习
CreateAMind
25+阅读 · 2019年1月4日
meta learning 17年:MAML SNAIL
CreateAMind
11+阅读 · 2019年1月2日
推荐|深度强化学习聊天机器人(附论文)!
全球人工智能
4+阅读 · 2018年1月30日
Arxiv
6+阅读 · 2019年7月11日
Arxiv
4+阅读 · 2018年5月10日
VIP会员
相关VIP内容
100+篇《自监督学习(Self-Supervised Learning)》论文最新合集
专知会员服务
159+阅读 · 2020年3月18日
【CMU】机器学习导论课程(Introduction to Machine Learning)
专知会员服务
58+阅读 · 2019年8月26日
Top
微信扫码咨询专知VIP会员