User Satisfaction Modeling with Domain Adaptation in Task-oriented Dialogue Systems (2022)

Abstract: User Satisfaction Estimation (USE) is crucial in helping measure the quality of a task-oriented dialogue system. However, the complex nature of implicit responses poses challenges in detecting user satisfaction, and most datasets are limited in size or not available to the public due to user privacy policies. Unlike task-oriented dialogue, large-scale annotated chitchat with emotion labels is publicly available. Therefore, we present a novel user satisfaction model with domain adaptation (USMDA) to utilize this chitchat. We adopt a dialogue Transformer encoder to capture contextual features from the dialogue. And we reduce domain discrepancy to learn dialogue-related invariant features. Moreover, USMDA jointly learns satisfaction signals in the chitchat context with user satisfaction estimation, and user actions in task-oriented dialogue with dialogue action recognition. Experimental results on two benchmarks show that our proposed framework for the USE task outperforms existing unsupervised domain adaptation methods. To the best of our knowledge, this is the first work to study user satisfaction estimation with unsupervised domain adaptation from chitchat to task-oriented dialogue.

## introduction the developed task-oriented dialogue system has achieved great success for various business situations, such as virtual assistants and informationseeking systems with domain knowledge @xcite . however, a dialogue chatbot with limited model capability sometimes fails to understand queries correctly and even annoys users with the wrong response. user satisfaction estimation (use) is able to detect user satisfaction and enable adjustment of the strategy of the system. @xcite implemented a smooth handoff from the machine to a human agent when use estimates a negative emotion from a user. when use detects good user feedback in the deployment environment, chatbots can utilize this information to learn and improve continuously @xcite . in recent years, the use in dialogue systems is always considered in the classification task. previous works @xcite show that data-driven pre-trained models can learn good exchange-level representations from task-oriented corpora and predict correct user satisfaction. unfortunately, most user satisfaction datasets are very limited in size @xcite or not publicly available due to user privacy policies @xcite . moreover, it is timeconsuming and expensive to conduct human evaluation experiments or crowd-sourcing for user satisfaction in a real-world task-oriented application. compared to the task-oriented dataset, the chitchat corpora from social media is easy-to-get but without explicit chatting targets. the underlying difference in linguistic patterns between the chitchat and task-oriented dialogue makes it difficult to utilize the chitchat dataset in the use task directly. therefore, unsupervised domain adaptation from chitchat to task-oriented dialogue is valuable and challenging in user satisfaction tasks. as shown in figure 1 , we collect two dialogue sessions from human-human chitchat and humanmachine task-oriented dialogue. in the chitchat, people talk around one topic and explicitly express their intents with emotions. in task-oriented dialogue, the user and system have explicit actions where the user wants to achieve his goal, and the system uses the background knowledge following the presetting actions. but users tend to implicitly show their emotions and are comfortable with the fulfillment of their goals. chitchat i wanna be buried at sea, it looks like fun. listen to me! when my time comes, i wanna be buried at sea. you what? task-oriented dialogue hello! can you help me find a hotel room? what other options do i have? may i suggest 1 hotel brooklyn bridge? it is a well reviewed, 4 star hotel. emotion neutral scared joyful action satisfaction inform intent offer request alternatives neutral -dissatisfied satisfaction neutral dissatisfied satisfied figure 1: two example dialogue sessions in chitchat (zahiri and choi, 2018) and task-oriented dialogue (rastogi et al., 2020). with unsupervised domain adaptation from chitchat to task-oriented dialogue. on the one hand, the model reduces the domain discrepancy of turn representations between chitchat and task-oriented dialogue datasets. on the other hand, the model learns satisfaction signals in context features from chitchat, and learns user actions in the task-oriented system with an additional dialogue action recognition (dar) task. moreover, the framework utilizes the pseudo-labeling approach @xcite to label the most confident predictions and build a stronger use model. to the best of our knowledge, our paper is the first attempt to explore the use with domain adaptation from chitchat to task-oriented dialogue. in this work, we make the following contributions: ## problem definition we formulate the task of user satisfaction estimation with domain adaptation from chitchat to taskoriented dialogue. given a set of chitchat and taskoriented dialogue sessions, each session contains n utterances {u 1 , u 2 , ..., u n }. we split the n utterances into n 2 exchange turns x @xmath0, u 2i ). each exchange turn is a communication either between multiple users or between user and system. each exchange turn in chitchat is annotated with a satisfaction label y s i and each exchange turn in a task-oriented dialogue has a user action a t i . our goal is to train a use model using labeled chitchat data s and unlabeled task-oriented dialogue data t to predict the correct satisfaction label y t i on t . ## framework this section introduces how to train a user satisfaction model with unsupervised domain adaptation. figure 2 shows the overall architecture of our proposed framework usmda with four different parts, including (1) dialogue transformer encoder to capture a representation of each exchange-turn in the dialogue, (2) joint learning for use with dar, (3) reducing domain discrepancy between different distributed datasets, (4) predicting pseudo labels in the task-oriented dialogue, and retraining the model with the top-k pseudo labels. ## conclusion we adopt joint-learning, mmd, and pseudolabeling with domain adaptation to improve the strong use model in task-oriented dialogue. the results show that domain adaptation with user actions is effective in the use task. mmd has positive effects on overall performance by learning domain-invariant dialogue-related feature representations. the pseudo-labeling is important for use with unsupervised domain adaptation. our proposed usmda framework has comparable results like the supervised model, encouraging future work addressing domain adaptation in the use task.