  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.

Computation and Language;Computer Vision and Pattern Recognition;Multimedia