Towards Universal Dialogue State Tracking

Abstract

Dialogue state tracking is the core part of a spoken dialogue system. It estimates the beliefs of possible user's goals at every dialogue turn. However, for most current approaches, it's difficult to scale to large dialogue domains. They have one or more of following limitations: (a) Some models don't work in the situation where slot values in ontology changes dynamically; (b) The number of model parameters is proportional to the number of slots; (c) Some models extract features based on hand-crafted lexicons. To tackle these challenges, we propose StateNet, a universal dialogue state tracker. It is independent of the number of values, shares parameters across all slots, and uses pre-trained word vectors instead of explicit semantic dictionaries. Our experiments on two datasets show that our approach not only overcomes the limitations, but also significantly outperforms the performance of state-of-the-art approaches.

Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation

“…Since the actual inference time multiplier roughly of the same magnitude as the theoretical value of 2.15, we can confirm empirically that we have the O(1) inference time complexity and thus obtain full scalability to the number of slots and values pre-defined in an ontology. Baselines 70.8% 25.83% O(mn) NBT-CNN 84.2% -O(mn) StateNet PSI (Ren et al, 2018) 88.9% -O(n) GLAD (Nouri and Hosseini-Asl, 2018) 88 , while the baseline for the MultiWoZ dataset is taken from the official website of MultiWoZ .…”

“…NBT-CNN O(mn) MD-DST (Rastogi et al, 2017) O(n) GLAD O(mn) StateNet PSI (Ren et al, 2018) O(n) TRADE (Wu et al, 2019) O(n) HyST (Goel et al, 2019) O(n) DSTRead (Gao et al, 2019) O(n) domain dialogue state tracking dataset, MultiWoZ , 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.…”

Scalable Neural Dialogue State Tracking

“…Apart from the architecture, the number of slots and values of the domain ontology also affects the time complexity of the DST. Recent works [7,9,8] use RNNs to obtain very high performance for DST, but nevertheless are quite limited as far as the efficiency of the models are concerned. For instance, the GCE model [10] addresses time complexity within the same architectural framework used by of GLAD [9], although the latency prediction of the model is still quite poor, at least for a production system (more details in Section 5).…”

“…Joint Goal Turn Request Delexicalisation Model [7] 70.8 87.1 NBT -CNN [7] 84.2 91.6 NBT -DNN [7] 84.4 91.2 CNN [16] 86.9 95.4 GLAD [9] 88.1 97.1 GCE [10] 88.5 97.4 StateNet PSI [8] 88.9 -Our Approach (G-SAT) 88.7 96.9 Table 1. Dialog state tracking results on the WOZ2.0 English testset.…”

“…model [16] is the only approach that does not use pre-trained embeddings, although they use also the development data for model training. GLAD [9], GCE [10] and StateNet PSI [8] are based on recurrent neural networks and use pre-trained embeddings. To facilitate comparison, our G-SAT approach is trained with the same pre-trained embeddings as used in GLAD and GCE.…”