Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment (2020)

Abstract: Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90% per-turn accuracies with only 10 dialogs on the extended-bAbI dataset.

## introduction end-to-end neural models have shown a great potential in building flexible goal-oriented dialog systems. they can be directly trained on past dialogs without any domain-specific handcrafting, which makes it easy to automatically scale up to new domains @xcite . however, these models are normally data-hungry and have only been successfully applied to domains with rich datasets @xcite @xcite . in real-world scenarios, common issues with end-to-end dialog models include: (1) the shortage of proper training dialogs because of the high cost of data collection and cleaning, i.e., the data scarcity problem @xcite , and (2) a large gap between limited data and unknown online test examples, i.e., the covariate shift effect @xcite . such problems can lead to a significant performance degradation in dialog systems, which may harm the users' experience and result in loss of customers in commercial applications. therefore, both fast adaptability and reliable performance are strongly desirable for practical system deployment. fast adaptability reflects the efficiency of adapting dialog systems to domains with low-resource data. reliable performance reflects the robustness of handling unpredictable user behaviors in online services. to boost the online performance of dialog systems, there have been some recent work @xcite @xcite on designing end-to-end models in a human-machine joint-teaming manner. for instance, the dialog system in @xcite can identify an ongoing dialog during testing when the system might fail and transfer it to a human agent. but all these methods are trained with sufficient data, which hinders the possibility of rapidly prototyping the models in new domains with restricted resources. in this paper, we formulate the low-resource goal-oriented dialog learning as a few-shot learning problem, where a limited numbers of dialogs are used for training and the remaining for the test. we propose the meta-dialog system (mds), an end-toend human-machine teaming framework optimized by the model-agnostic meta-learning (maml) algorithm @xcite . in general, mds learns to make prediction and requests human by finding good initial parameters, which can be adapted to new tasks fast and reliably by using fewer dialogs. we evaluate our methods on a new multi-domain dialog dataset called extended-babi. results show that mds achieves obvious performance improvement over baselines and attains more than 90% per-turn accuracy on new domains with only 10 dialogs. we also perform experiments on multiwoz dataset @xcite which has been transformed into simplified babi format and observe similar superior results with mds. in summary, the main contributions of this paper are three-fold: (1) to the best of our knowledge, this is the first study on applying meta-learning to retrieval-based end-to-end goal-oriented dialog systems; (2) we leverage the maml algorithm to optimize a human-machine collaborative dialog system and show very promising results on the lowresource dialog tasks; and (3) we propose a new dataset and hope that can help bring forward the research in this area. ## the proposed method in this section, we first introduce the problem definition and our new dataset; we then elaborate the framework of mds and meta-learning procedures. problem definition. we focus on the retrievalbased goal-oriented dialog tasks @xcite , where a training data d i usually contains a triple (h i , y i , r). h i denotes the dialog history consisting of all user utterances and system responses up to the current turn, r is a set of given candidate responses and y i is the index of the correct response in r. the main task is to train an end-to-end dialog model to predict y i from r based on h i . extended-babi dataset. the original babi dataset @xcite is not suitable for lowresource settings due to the lack of domains and tasks. we extend it into a multi-domain dataset through complicated simulation rules and construct templates with a more diversity to raise the difficulty. there are 7 domains in total: restaurant, flights, hotels, movies, music, tourism and weather, each of which has its own ontology and the candidate response set. similar to @xcite , a complete dialog in extended-babi contains four phases of interactions: (1) the system asks for required attributes to constrain the search and issues the first api call; (2) the user updates their requests for revised api calls; (3) the system confirms for multiple times to determine the entity the user wants; (4) the user requests more attributes for extra information based on the final entity. the total number of dialogs is 21,000 and the detailed examples and statistics are given in appendix a.1. ## experiments and results in our experiments, we first verify the capability of mds on our newly simulated dialog dataset extended-babi, and then conduct extra evaluation on the more realistic dataset multiwoz 2.1 @xcite . ## related work end-to-end neural approaches of building dialog systems have attracted increasing research interest. the work of @xcite is the first attempt to solve goal-oriented dialog tasks with endto-end models. further improvements has been made in @xcite to combine explicit domain-specific knowledge and implicit rnn features. @xcite take user personalities into consideration for better user satisfaction. @xcite learn dialogs with multiple possible answers. our work is inspired by the work of @xcite , which propose to solve unseen user behaviors through human-machine teamwork. the research of @xcite @xcite also show the advantages of incorporating the role of human to teach online. however, dialog learning in lowresource scenarios has not been investigated. meta-learning aims to learn new tasks rapidly with a few training examples @xcite , which fits well to our task. there have been some work applying meta-learning to other tasks in dialog research, such as that in @xcite for natural language understanding and @xcite for natural language generation. ## conclusion and future work in this paper, we leverage the maml algorithm to optimize a human-machine collaborative dialog system, which shows good results for both fast adaptability and reliable performance. in the future, we plan to use more powerful encoders and evaluate our methods on real dialog data.