Abstract: Existing approaches to dialogue state tracking rely on pre-defined ontologies consisting of a set of all possible slot types and values. Though such approaches exhibit promising performance on single-domain benchmarks, they suffer from computational complexity that increases proportionally to the number of pre-defined slots that need tracking. This issue becomes more severe when it comes to multi-domain dialogues which include larger numbers of slots. In this paper, we investigate how to approach DST using a generation framework without the pre-defined ontology list. Given each turn of user utterance and system response, we directly generate a sequence of belief states by applying a hierarchical encoder-decoder structure. In this way, the computational complexity of our model will be a constant regardless of the number of pre-defined slots. Experiments on both the multi-domain and the single domain dialogue state tracking dataset show that our model not only scales easily with the increasing number of pre-defined domains and slots but also reaches the state-of-the-art performance.

Introduction: A Dialogue State Tracker (DST) is a core component of a modular task-oriented dialogue system BIBREF7 . For each dialogue turn, a DST module takes a user utterance and the dialogue history as input, and outputs a belief estimate of the dialogue state. Then a machine action is decided based on the dialogue state according to a dialogue policy module, after which a machine response is generated. Traditionally, a dialogue state consists of a set of requests and joint goals, both of which are represented by a set of slot-value pairs (e.g. (request, phone), (area, north), (food, Japanese)) BIBREF8 . In a recently proposed multi-domain dialogue state tracking dataset, MultiWoZ BIBREF9 , a representation of dialogue state consists of a hierarchical structure of domain, slot, and value is proposed. This is a more practical scenario since dialogues often include multiple domains simultaneously. Many recently proposed DSTs BIBREF2 , BIBREF10 are based on pre-defined ontology lists that specify all possible slot values in advance. To generate a distribution over the candidate set, previous works often take each of the slot-value pairs as input for scoring. However, in real-world scenarios, it is often not practical to enumerate all possible slot value pairs and perform scoring from a large dynamically changing knowledge base BIBREF11 . To tackle this problem, a popular direction is to build a fixed-length candidate set that is dynamically updated throughout the dialogue development. cpt briefly summaries the inference time complexity of multiple state-of-the-art DST models following this direction. Since the inference complexity of all of previous model is at least proportional to the number of the slots, these models will struggle to scale to multi-domain datasets with much larger numbers of pre-defined slots. In this work, we formulate the dialogue state tracking task as a sequence generation problem, instead of formulating the task as a pair-wise prediction problem as in existing work. We propose the COnditional MEmory Relation Network (COMER), a scalable and accurate dialogue state tracker that has a constant inference time complexity.  Specifically, our model consists of an encoder-decoder network with a hierarchically stacked decoder to first generate the slot sequences in the belief state and then for each slot generate the corresponding value sequences. The parameters are shared among all of our decoders for the scalability of the depth of the hierarchical structure of the belief states. COMER applies BERT contextualized word embeddings BIBREF12 and BPE BIBREF13 for sequence encoding to ensure the uniqueness of the representations of the unseen words. The word embeddings for sequence generation are initialized and fixed with the static word embeddings generated from BERT to have the potential of generating unseen words.

Question: Does this approach perform better in the multi-domain or single-domain setting?
Answer: single-domain setting