A Unified Dialogue User Simulator for Few-shot Data Augmentation (2022)

Abstract: Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment large-scale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with few-shot data. The experiments on a target dataset across multiple domains show that our proposed model brings remarkable performance increases through data augmentation.

## introduction building a dialogue system typically requires seeing many dialogue samples to fully explore the state-action space and language variations @xcite . however, collecting a large multi-turn dialogue dataset for a specific domain with crowd-sourced workers is time-consuming and labor-intensive. dialogue user simulation, a technique that builds an agent to mimic the behaviors of real human users, is an economical alternative, providing unlimited usersystem interactions at a low cost @xcite @xcite @xcite . generally speaking, there are two ways to use a user simulator to alleviate the data scarcity problem: 1) the simulator serves as an interactive environment to train a system agent directly using reinforcement learning; 2) the simulator is paired with a system agent to generate synthetic dialogues that can be used in dialogue model training. although the former is popular in traditional dialogue goal: you want to book a taxi. the taxi should go to anatolia and should leave after 22:00. the taxi should depart from kymmoy. make sure you get car type and contact number. user: i need a taxi to |anatolia|>. system: what time would you like to leave? user: i would like to leave after |22:00|>. system: there are |9|> options available to you. where would you like to leave from? user: i 'd like to leave from |kymmoy|>. system: the fare estimate is |9.78|>. do you want to book this taxi now? user: no, i would like the contact number if possible. system: the contact number is |07356725299|> user: thank you very much for your help. system: you are welcome. do you need anything else today early works on user simulation mainly focus on the user policy module, which perceives and responses in dialogue act level. however, these user simulators, either rule-based @xcite , statistical-based @xcite , or neural-based @xcite , require finegrained dialogue act design/annotation and ignore the noise introduced by semantic parsing and language generation. therefore, @xcite proposed an rnn-based natural language level user simulator that generates a user utterance according to the context directly. however, such a data-driven approach requires enough data in the target domain, which is less effective in the lowresource scenario. recently proposed large-scale pre-trained language models (plms) @xcite provide a solution to few-shot user modeling, since they have achieved great suc-cess through transfer learning on a wide range of nlp tasks, including few-shot dialogue modeling @xcite . moreover, the performance can be further improved by pre-training on a large dialogue corpus before fine-tuning @xcite @xcite . as for dialogue simulation, @xcite used gpt-2 @xcite to model user and system separately and generated dialogues through self-chat. the user generates an utterance according to the user goal in natural language and dialogue context, while the system first generates a query to retrieve entities from a database and then generates a delexicalized response. @xcite proposed to directly generate a whole dialogue with a single bart model @xcite given the user goal and candidate entities from the database. however, both methods require domain-specific database interaction, which prevents the simulators from using large scale dialogue corpora for domain-adaptive pre-training. in this work, we propose an end-to-end user simulator that only takes user goal and dialogue context as input. in order to alleviate the data scarcity problem in the target domain, we pre-train the simulator on a collection of public dialogue datasets of different domains and annotation schema. we also pre-train a context-to-response model without database interaction on similar datasets as the system agent. note that one could replace this simple model with any system agent such as pptod @xcite , a powerful end-to-end model in fewshot scenario. then we fine-tune both agents on the data of target domain and generate synthetic dialogues through self-chat, as shown in we conduct both automatic and human evaluation on the quality of synthetic dialogues and show that our method can generate more goal-consistent dialogues than baselines with limited data in the target domain. we also show that using the synthetic dialogues can better improve few-shot dialogue state tracking than baselines. our contributions are summarized as follows: two lines of works are relevant to this study: neural user simulation and pre-trained language model. we briefly discuss their connections and differences to our work. ## experiment 4.1 data augmentation to generate synthetic dialogues in a target domain, we first fine-tune both user and system agents on a few samples in that domain. we use multiwoz 2.1 @xcite as the target dataset and tune the models with the same hyper-parameters except a smaller @xmath0. to examine whether the simulator is endowed with the general conversational ability and can quickly be adapted to new scenarios with limited data, we set the available dialogues to 5%/10% of the original training set of the target dataset. then we sample user goals from the remaining training set and generate dialogues through self-chat. we set the decoding hyper-parameters of both model to: @xmath1, @xmath2.9, @xmath3. ## conclusion in this paper, we develop a unified dialogue simulation base model. we collect a collection of representative open-sourced task-oriented dialogue datasets and transform them into a unified format, including task description, user goal, and dialogue context. through domain adaptive pre-training on those datasets, our proposed model successfully learns the essential conversation ability and skills of a user and can be easily adapted to a downstream task by using only a small amount of target data. to examine the performance of our model, we use it to generate synthesized dialogues and conduct dialogue state tracking experiments. the results demonstrate that our proposed model brings remarkable performance increases through data augmentation. we believe our proposed method can foster future research on dialogue simulation and data augmentation on low-resource task-oriented dialogue tasks.