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<!DOCTYPE html>
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<title>STICKERCONV: Generating Multimodal Empathetic Responses from Scratch</title>
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<h1 class="title is-1 publication-title">S<span style="font-size: smaller;">TICKER</span>C<span style="font-size: smaller;">ONV</span>: Generating Multimodal Empathetic Responses from Scratch</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://github.com/ZhangYiqun018" target="_blank">Yiqun Zhang</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://friedrichor.github.io/" target="_blank">Fanheng Kong</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://github.com/Control-derek" target="_blank">Peidong Wang</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://github.com/NanShanhai" target="_blank">Shuang Sun</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://github.com/WangLingS" target="_blank">Lingshuai Wang</a><sup>1</sup></span>
<br>
<span class="author-block">
<a href="https://neu-datamining.github.io/cse/fengshi/" target="_blank">Shi Feng</a><sup>1†</sup>,</span>
<span class="author-block">
<a href="https://neu-datamining.github.io/wangdl.htm" target="_blank">Daling Wang</a><sup>1</sup>,</span>
<span class="author-block">
<a href="http://faculty.neu.edu.cn/zhangyifei/zh_CN/index.htm" target="_blank">Yifei Zhang</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com.hk/citations?hl=zh-CN&user=Ms678voAAAAJ" target="_blank">Kaisong Song</a><sup>2</sup></span>
</div>
<div class="is-size-6 publication-authors">
<span class="author-block"><sup>*</sup>Equal Contribution</span> <sup>†</sup>Corresponding Author</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>School of Computer Science and Engineering, Northeastern University, Shenyang, China</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>2</sup>Alibaba Group, Hangzhou, China</span>
</div>
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<a href="https://arxiv.org/abs/2402.01679" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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</span>
<span>Paper</span>
</a>
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<a href="https://github.com/ZhangYiqun018/StickerChat" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
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<span>Code</span>
</a>
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<a href="https://huggingface.co/datasets/NEUDM/StickerConv" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<span>Dataset</span>
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<span>Wechat@CCAC</span>
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<span class="publication-venue">ACL 2024 Main</span>
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<!-- Paper abstract -->
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<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Stickers, while widely recognized for enhancing empathetic communication in online interactions, remain underexplored in
current empathetic dialogue research, notably due to the challenge of a lack of comprehensive datasets. In this paper, we
introduce the Agent for S<span style="font-size: smaller;">TICKER</span>C<span style="font-size: smaller;">ONV</span> (Agent4SC),
which uses collaborative agent interactions to realistically simulate human behavior with sticker usage, thereby enhancing
multimodal empathetic communication. Building on this foundation, we develop a multimodal empathetic dialogue dataset,
S<span style="font-size: smaller;">TICKER</span>C<span style="font-size: smaller;">ONV</span>, comprising 12.9K dialogue sessions,
5.8K unique stickers, and 2K diverse conversational scenarios. This dataset serves as a benchmark for multimodal empathetic
generation. To advance further, we propose <b>PE</b>rceive and <b>G</b>enerate <b>S</b>tickers (PEGS), a multimodal empathetic
response generation framework, complemented by a comprehensive set of empathy evaluation metrics based on LLM. Our experiments
demonstrate PEGS's effectiveness in generating contextually relevant and emotionally resonant multimodal empathetic
responses, contributing to the advancement of more nuanced and engaging empathetic dialogue systems.
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<h2 class="title is-2">Technical Description</h2>
<br>
</div>
<!-- Agent -->
<div class="columns is-centered">
<div class="column is-full-width">
<h4 class="title is-3">• Agent for S<span style="font-size: smaller;">TICKER</span>C<span style="font-size: smaller;">ONV</span> (Agent4SC)</h4>
<div class="content has-text-justified">
<img class="columns is-centered has-text-centered" src="./static/images/Agent4SC.png"
alt="Teaser" width="95%" style="margin:0 auto">
<br>
<figcaption>
<p style="text-align: justify;">
<font color="061E61">
<b>Figure 1:</b> The overview of Agent4SC. Memory and Plan modules enable the agent to mimic human observation and
thought, overcoming LLMs' inability to grasp nuanced emotions. The Action module supports generating insights with
human-like emotional reactions. The Profile module gives each agent distinct reflections and actions. Furthermore,
Agent4SC uses stickers as a Tool for more natural conversation, allowing the agent to choose stickers like humans.
These modules streamline observation, reflection, and action, while the Manager Agent maintains performance and quality.
</font>
</p>
</figcaption>
</div>
</div>
</div>
<!-- Dataset -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">• S<span style="font-size: smaller;">TICKER</span>C<span style="font-size: smaller;">ONV</span></h2>
<div class="content has-text-justified">
<img class="columns is-centered has-text-centered" src="./static/images/StickerConv_example.png"
alt="Teaser" width="40%" style="margin:0 auto">
<br>
<figcaption>
<p style="text-align: justify;">
<font color="061E61">
<b>Figure 2:</b> An example of multimodal conversation in our S<span style="font-size: smaller;">TICKER</span>C<span style="font-size: smaller;">ONV</span> dataset.
Both parties can utilize the stickers to express their emotions, which enhances interactivity and expression.
Assistant can empathize with the user according to the conversation (<span style="color: rgb(0, 153, 0);">green</span> text).
</font>
</p>
</figcaption>
<br>
<img class="columns is-centered has-text-centered" src="./static/images/StickerConv_statistics.png"
alt="Teaser" width="50%" style="margin:0 auto">
<br>
<figcaption>
<p style="text-align: justify;">
<font color="061E61">
<b>Figure 3:</b> The statistics of S<span style="font-size: smaller;">TICKER</span>C<span style="font-size: smaller;">ONV</span>.
</font>
</p>
</figcaption>
<br>
<img class="columns is-centered has-text-centered" src="static/images/StickerConv_compare_user_system.png"
alt="Teaser" width="50%" style="margin:0 auto">
<br>
<figcaption>
<p style="text-align: justify;">
<font color="061E61">
<b>Figure 4:</b> The chart of emotional distribution in the choice of stickers between users and the system.
This chart revealed a striking trend: users have a significant preference for stickers that convey negative emotions,
in contrast to the system. The system predominantly utilizes stickers expressing neutral and positive emotions.
This comparison not only reflects the distinct emotional expression preferences between users and the system
but also highlights the system's active and supportive role in interactions.
</font>
</p>
</figcaption>
</div>
<br>
<div class="columns is-centered">
<div class="column is-half">
<img class="columns is-centered has-text-centered" src="static/images/StickerConv_topic_emotion_dist.png"
alt="Teaser" width="90%" style="margin:0 auto">
<br>
<figcaption>
<p style="text-align: justify;">
<font color="061E61">
<b>Figure 5:</b> Emotion distribution of user profile in Agent for S<span style="font-size: smaller;">TICKER</span>C<span style="font-size: smaller;">ONV</span>.
</font>
</p>
</figcaption>
</div>
<div class="column is-half">
<img class="columns is-centered has-text-centered" src="static/images/StickerConv_wordclound.png"
alt="Teaser" width="90%" style="margin:0 auto">
<br>
<figcaption>
<p style="text-align: justify;">
<font color="061E61">
<b>Figure 6:</b> The 200 most popular emotion-related words in S<span style="font-size: smaller;">TICKER</span>C<span style="font-size: smaller;">ONV</span>.
</font>
</p>
</figcaption>
</div>
</div>
</div>
</div>
<!-- PEGS -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">• PEGS</h2>
<div class="content has-text-justified">
<img class="columns is-centered has-text-centered" src="./static/images/PEGS.png"
alt="Teaser" width="80%" style="margin:0 auto">
<br>
<figcaption>
<p style="text-align: justify;">
<font color="061E61">
<b>Figure 7:</b> The architecture of PEGS framework includes various routing options, distinguished by colored connecting
lines. Input stickers undergo joint encoding by an image encoder, Q-Former, and a linear layer, with Vicuna serving as the
language model. The output of the LLM activates two sets of tokens differently across model versions: one for image retrieval
and the other as a textual condition. Subsequently, the frozen image decoder generates images.
</font>
</p>
</figcaption>
</div>
</div>
</div>
</div>
</section>
<!-- Results -->
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<h2 class="title is-2">Results</h2>
<br>
</div>
<!-- Conversation -->
<div class="columns is-centered">
<div class="column is-full-width">
<div class="content has-text-justified">
<img class="columns is-centered has-text-centered" src="static/images/conversation.png"
alt="Teaser" width="95%" style="margin:0 auto">
<br>
<figcaption>
<p style="text-align: justify;">
<font color="061E61">
<b>Figure 8:</b> Examples of conversations by users interacting with PEGS.
Users can chat with multimodal content (text and stickers) and will receive multimodal empathetic responses.
Left: a conversation characterized by positive emotion (happiness).
Right: a conversation characterized by negative emotion (sadness).
</font>
</p>
</figcaption>
</div>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>
@article{zhang2024stickerconv,
title={STICKERCONV: Generating Multimodal Empathetic Responses from Scratch},
author={Zhang, Yiqun and Kong, Fanheng and Wang, Peidong and Sun, Shuang and Wang, Lingshuai and Feng, Shi and Wang, Daling and Zhang, Yifei and Song, Kaisong},
journal={arXiv preprint arXiv:2402.01679},
year={2024}
}
</code></pre>
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</section>
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