Energy-based Neural Modelling for Large-Scale Multiple Domain Dialogue State Tracking (2020)

Abstract: Scaling up dialogue state tracking to multiple domains is challenging due to the growth in the number of variables being tracked. Furthermore, dialog state tracking models do not yet explicitly make use of relationships between dialogue variables, such as slots across domains. We propose using energy-based structure prediction methods for large-scale dialogue state tracking task in two multiple domain dialogue datasets. Our results indicate that: (i) modelling variable dependencies yields better results; and (ii) the structured prediction output aligns with the dialogue slot-value constraint principles. This leads to promising directions to improve state-of-the-art models by incorporating variable dependencies into their prediction process.

## introduction task-oriented dialogue systems have been developed to assist users in many fields @xcite . in recent years it is a rising trend to scale-up task-oriented dialogue systems from single domain to multiple domains to improve the generalisability of models and support transfer of knowledge across domains. this leads to a new challenge in handling dialogues in the multi-domain context, that in turns increases the work load of the dialogue manager, and in particular the dialogue state tracking component. on the other hand, a number of works have demonstrated the benefit of processing multiple domains, for example it has been shown that such models yield better performances across domains in comparison with single domain trackers constructed and trained with the same approach @xcite . dialogue state tracking in task-oriented dialogue systems frequently uses a multi-slot representation for the dialogue state, thus casting the task as a multi-task classification problem. in these scenar-ios, an increase in the number of domains is equivalent to an increase in the number of slots, this in turn enlarges the models and makes the task more challenging. while traditionally one can develop a number of models to track dialogue states in each domain separately, recent advanced techniques tend to train dialogue state trackers in the multi-domain environment. such multi-domain trackers produce state-of-the-art results @xcite . to date state-of-the-art dialogue state trackers have treated the task as a set of individual domaindependent classification problems @xcite @xcite . however, we argue that such approaches leave room for improvement; particularly with the consideration of the nature of human-machine interactions @xcite . specially, we argue that the multi-task classification methodology usually does not take into account the relationships between dialogue slot variables, despite the fact that these factors can play an essential part in the dialogue state prediction @xcite . therefore, we propose to explicitly incorporate dialogue variable associations into the prediction process in a multi-domain dialogue environment, thus casting the dialogue state tracking task a structured prediction problem. in this paper we demonstrate the manner, in which dialogue variable dependencies make an impact on the dialogue state tracking process in a multiple domain context. we choose two newly published multiple domain datasets, multiwoz 2.0 @xcite and multiwoz 2.1 @xcite , to conduct our study. these datasets contain a large number of dialogues across several different domains, thus they are practical for our study. our investigation is detailed in three stages: to the best of our knowledge, there have been structured prediction models developed for dialogue state tracking in single domains, but no work has been performed for multiple domains. on the other hand, several multi-domain dialogue state trackers study the topic of variable dependencies to some extent, but do not provide a detailed analysis on this phenomenon. therefore, the contributions of our work are two-fold: (i) a large-scale structured prediction model for multi-domain dialogue state tracking; and (ii) a systematic analysis of variable dependencies across dialogue slots and domains. the work presented in this paper is an empirical research of our previous work on capturing variable dependencies in dialogue states within single dialogue domains @xcite . we demonstrate that the energy-based method has good generalisability when applied to track dialogue states in multiple domain settings. ## variable associations in multi-domain dialogue there are a number of works that to some extent have studied the variable associations in dialogue data in both single and multiple domain contexts. single-domain dialogue variable dependencies were explicitly studied in the work by @xcite . the associations between slots in single domain dialogue data are demonstrated to be beneficial factors for dialogue state tracking, and structured prediction approaches such as energy-based learning are effective in studying this phenomenon. on the other hand, although there has been no explicit study on variable dependencies in multiple domain dialogue data, we can indirectly infer the benefit of modelling such dependencies. @xcite show that shared models across dialogue domains yield better results than their domain-specific counterparts. similarly in the trade model, @xcite highlighted the correlations between domains by training the base model on all of the domains except one, then fine-tuning on the remaining domain. since we focus on multiple domain dialogue state tracking, we conduct our study on multiwoz 2.0 @xcite and multiwoz 2.1 @xcite , two novel chat-based multidomain dialogue datasets. we perform statistical tests on the dialogue data, and present the data analysis results in figure 1 . the statistical tests are pearson's chi-squared test, which is useful for detecting pairwise dependencies between variables, and the chi-square test-based cramer's v measurement, that measures the dependency strength once confirmed @xcite . in figure 1 we present the heatmap of measured cramer's v between all slot pairs in multiwoz 2.1 dataset, since this dataset contains manually fixed labels based on multiwoz 2.0 data. the analysis explicitly confirms the variable dependencies in the multiple domain dialogue data, where pairwise statistical significance coefficient @xmath0.05 for all slot pairs. these dependencies exist on both slot and domain levels. our analysis results also align to some extent with the cosine similarity of slot embedding presented in the trade model @xcite . ## energy-based learning dialogue state tracking energy-based learning @xcite is an approach to structured prediction that can be used to account for variable dependencies in a supervised learning process. the core concept of the approach is to represent the associations of all variables in the system with a scalar value called energy, and to train an energy function that assigns low energy values to valid combinations of variables. there are two key functions in the energy-based dialogue state tracker that we have developed: the working mechanism of an energy-based model is different from a standard feedforward deep learning model: ## results & discussion we evaluate the performance of both our multitask feature system and the energy-based tracker with an accuracy metric as is common in dialogue state tracking. the results are reported in overall, our energy-based dialogue state tracker yields competitive results in comparison to models that account for variable relationships using techniques such as attention mechanism @xcite and transfer learning @xcite . when accounting for the variable dependencies with the energy-based method, we improve the belief state tracking results by large margins, i.e., 13.9% for multiwoz 2.0 and 18.1% for multiwoz 2.1. we believe that there are at least two reasons for this large improvement: one final observation with respect to the results is differences in performance across multiwoz 2.0 and 2.1 datasets. even though the labels in multiwoz 2.1 dataset are corrected with manual labour, meaning the data is less noisy than the multiwoz 2.0 data, not all systems yield better results in multiwoz 2.1 than in multiwoz 2.0, e.g., models such as trade @xcite and dstqa @xcite perform better with the original noisy data. in contrast, we observe that other state-of-the-art systems includ-ing our energy-based tracker perform better with cleaner data (multiwoz 2.1); this is of course a common phenomenon in supervised learning. ## conclusion in this paper we demonstrated the effectiveness of applying the energy-based learning method to a large-scale dialogue state tracking task in multiple domains. we showed that the energy-based method is capable of capturing the dependencies between dialogue variables such as slots across domains, thus it improves the performance over a multi-task deep learning system significantly. our analyses also showed that the structured prediction method can produce dialogue states that follow dialogue slot-value constraint rules in contrast with a multilabel classification method. although the results achieved with the energybased method are competitive with published dialogue state tracking systems, they are not yet state of the art. there are several directions to investigate the further impact of an energy-based methodology on the dialogue state tracking task. one promising direction is the application of our energybased method on top of an existing state-of-the-art systems to further improve that system's performance. another direction is to refine the energybased structure and investigate various strategies for the learning and inference processes to improve the ability to integrate captured dependencies into the structured prediction at a higher level. furthermore our long term goal is to apply the structured learning approach in tracking different aspects of the conversations such as personality and preference as well as user intents.