Offensive language detection is increasingly crucial for maintaining a civilized social media platform and deploying pre-trained language models. However, this task in Chinese is still under exploration due to the scarcity of reliable datasets. To this end, we propose a benchmark --COLD for Chinese offensive language analysis, including a Chinese Offensive Language Dataset --COLDATASET and a baseline detector --COLDETECTOR which is trained on the dataset. We show that the COLD benchmark contributes to Chinese offensive language detection which is challenging for existing resources. We then deploy the COLDETECTOR and conduct detailed analyses on popular Chinese pre-trained language models. We first analyze the offensiveness of existing generative models and show that these models inevitably expose varying degrees of offensive issues. Furthermore, we investigate the factors that influence the offensive generations, and we find that anti-bias contents and keywords referring to certain groups or revealing negative attitudes trigger offensive outputs easier.

COLD: A Benchmark for Chinese Offensive Language Detection

Dialogue contradiction is a critical issue in open-domain dialogue systems. The contextualization nature of conversations makes dialogue contradiction detection rather challenging. In this work, we propose a benchmark for Contradiction Detection in Chinese Conversations, namely CDConv. It contains 12K multi-turn conversations annotated with three typical contradiction categories: Intra-sentence Contradiction, Role Confusion, and History Contradiction. To efficiently construct the CDConv conversations, we devise a series of methods for automatic conversation generation, which simulate common user behaviors that trigger chatbots to make contradictions. We conduct careful manual quality screening of the constructed conversations and show that state-of-the-art Chinese chatbots can be easily goaded into making contradictions. Experiments on CDConv show that properly modeling contextual information is critical for dialogue contradiction detection, but there are still unresolved challenges that require future research.

CDConv: A Benchmark for Contradiction Detection in Chinese Conversations

The capacity of empathy is crucial to the success of open-domain dialog systems.
Due to its nature of multi-dimensionality, there are various factors that relate to empathy expression, such as communication mechanism, dialog act and emotion.
However, existing methods for empathetic response generation usually either consider only one empathy factor or ignore the hierarchical relationships between different factors, leading to a weak ability of empathy modeling.
In this paper, we propose a multi-factor hierarchical framework, CoMAE, for empathetic response generation, which models the above three key factors of empathy expression in a hierarchical way.
We show experimentally that our CoMAE-based model can generate more empathetic responses than previous methods.
We also highlight the importance of hierarchical modeling of different factors through both the empirical analysis on a real-life corpus and the extensive experiments.
Our codes and used data are available at https://github.com/chujiezheng/CoMAE.

CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation

Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats. Following reasonable procedures and using various support skills can help to effectively provide support. However, due to the lack of a well-designed task and corpora of effective emotional support conversations, research on building emotional support into dialog systems remains lacking. In this paper, we define the Emotional Support Conversation (ESC) task and propose an ESC Framework, which is grounded on the Helping Skills Theory. We construct an Emotion Support Conversation dataset (ESConv) with rich annotation (especially support strategy) in a help-seeker and supporter mode. To ensure a corpus of high-quality conversations that provide examples of effective emotional support, we take extensive effort to design training tutorials for supporters and several mechanisms for quality control during data collection. Finally, we evaluate state-of-the-art dialog models with respect to the ability to provide emotional support. Our results show the importance of support strategies in providing effective emotional support and the utility of ESConv in training more emotional support systems.

Towards Emotional Support Dialog Systems

**Topics:** Commonsense Reasoning 
Dialogue and Interactive Systems 
Dialogue and Interactive Systems (TACL) 
Efficient Methods for NLP 
Speech, Vision, Robotics, Multimodal Grounding 
Ethics 
Information Extraction 
Interpretability, Interactivity and Analysis of Models for NLP 
Language Modeling and Analysis of Language Models

Poster Session 11

poster

## Welcome to EMNLP 2022!
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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

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O13 Dialog And Interactive Systems 1

technical paper

Findings Papers - Dialog and Interactive Systems

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As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

**Session Chair: ** Alborz Geramifard 
**Session Volunteer: ** Nischal Ashok

Chujie Zheng

4

2

SHORT BIO

Presentations

COLD: A Benchmark for Chinese Offensive Language Detection

CDConv: A Benchmark for Contradiction Detection in Chinese Conversations

CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation

Towards Emotional Support Dialog Systems

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