As the functionality of dialogue systems evolves, hybrid dialogue systems that accomplish user-specific goals and participate in open-topic chitchat with users are attracting growing attention. Existing research learns both tasks concurrently utilizing a multi-task fusion technique but ignores the negative transfer phenomenon induced by the unique textual style differences. Therefore, contrastive learning based on the latent variable model is used to decouple the various textual genres in the latent space. We devise supervised and self-supervised positive and negative sample constructions for diverse datasets. In addition, to capitalize on the style information contained in the decoupled latent variables, we employ a style prefix that incorporates latent variables further to control the generation of responses with varying styles. We performed extensive experiments on three dialogue datasets, including a hybrid dialogue dataset and two task-oriented dialogue datasets. The experimental results demonstrate that our method can mitigate the negative style transfer issue and achieves state-of-the-art performance on multiple dialogue datasets.

Mitigating Negative Style Transfer in Hybrid Dialogue System

Task-Oriented Dialogue (TOD) systems are drawing more and more attention in recent studies. Current methods focus on constructing pre-trained models or fine-tuning strategies while the evaluation of TOD is limited by a policy mismatch problem. That is, during evaluation, the user utterances are from the annotated dataset while these utterances should interact with previous responses which can have many alternatives besides annotated texts. Therefore, in this work, we propose an interactive evaluation framework for TOD. We first build a goaloriented user simulator based on pre-trained models and then use the user simulator to interact with the dialogue system to generate dialogues. Besides, we introduce a sentencelevel and a session-level score to measure the sentence fluency and session coherence in the interactive evaluation. Experimental results show that RL-based TOD systems trained by our proposed user simulator can achieve nearly 98% inform and success rates in the interactive evaluation of MultiWOZ dataset and the proposed scores measure the response quality besides the inform and success rates. We are hoping that our work will encourage simulatorbased interactive evaluations in the TOD task.

Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator

Task-Oriented Dialogue (TOD) systems are drawing more and more attention in recent studies.
Current methods focus on constructing pre-trained models or fine-tuning strategies while the evaluation of TOD is limited by a policy mismatch problem.
That is, during evaluation, the user utterances are from the annotated dataset while these utterances should interact with previous responses which can have many alternatives besides annotated texts.
Therefore, in this work, we propose an interactive evaluation framework for TOD. 
We first build a goal-oriented user simulator based on pre-trained models and then use the user simulator to interact with the dialogue system to generate dialogues.
Besides, we introduce a sentence-level and a session-level score to measure the sentence fluency and session coherence in the interactive evaluation. 
Experimental results show that RL-based TOD systems trained by our proposed user simulator can achieve nearly 98\% inform and success rates in the interactive evaluation of MultiWOZ dataset and the proposed scores measure the response quality besides the inform and success rates.
We are hoping that our work will encourage simulator-based interactive evaluations in the TOD task.

SNLP: Conversational AI/Dialogue Systems 2

poster

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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

**Chairs:** Zhijian Ou, Junlan Feng and Juanzi Li (remote) **Organizers:** Zhijian Ou, Junlan Feng and Juanzi Li 
**Invited speakers**: [Dilek Hakkani-Tur](https://underline.io/speakers/98583-dilek-hakkani-tur), [Jason Williams](https://underline.io/speakers/214050-jason-williams) and [Pascale Fung](https://underline.io/speakers/55657-pascale-fung)
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W10 (SereTOD) Towards Semi-Supervised and Reinforced Task-Oriented Dialog Systems

workshop paper

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EMNLP 2022

Welcome!
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.

Qinyuan Cheng

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Mitigating Negative Style Transfer in Hybrid Dialogue System

Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator

Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator

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