SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing

Hu, Jiaying; Yang, Yan; Chen, Chencai; He, Liang; Yu, Zhou

doi:10.18653/v1/2020.acl-main.567

Cited by 38 publications

(45 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the recent development of deep learning and representation learning, most works about DST focus on encoding dialogue context with deep neural networks and predicting a value for each possible slot (Xu and Hu, 2018;Zhong et al, 2018;. For multi-domain DST, slot-value pairs are extended to domain-slot-value pairs for the target Wu et al, 2019;Chen et al, 2020b;Hu et al, 2020;Heck et al, 2020;. These models greatly improve the performance of DST, but the mechanism of treating slots equally is inefficient and may lead to additional errors.…”

Section: Related Workmentioning

confidence: 99%

“…DS-DST uses two BERT-base encoders and takes a hybrid approach . SAS proposes a Dialogue State Tracker with Slot Attention and Slot Information Sharing to reduce redundant information's interference (Hu et al, 2020). SOM-DST considers the dialogue state as an explicit fixedsize memory and proposes a selectively overwriting mechanism (Kim et al, 2020).…”

Section: Baseline Modelsmentioning

confidence: 99%

“…These methods can be directly adapted to multi-domain conversations by replacing slots in a single domain with domain-slot pairs predefined. In multi-domain DST, some of the previous works study the scalability of the model (Wu et al, 2019), some aim to fully utilizing the dialogue history and context (Shan et al, 2020;Chen et al, 2020a;Quan and Xiong, 2020), and some attempt to explore the relationship between different slots (Hu et al, 2020;Chen et al, 2020b). Nevertheless, existing approaches generally predict the dialogue state at every turn from scratch.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking

Guo¹,

Shuang²,

Li³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

View full text Add to dashboard Cite

The goal of dialogue state tracking (DST) is to predict the current dialogue state given all previous dialogue contexts. Existing approaches generally predict the dialogue state at every turn from scratch. However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn. Therefore, the mechanism of treating slots equally in each turn not only is inefficient but also may lead to additional errors because of the redundant slot value generation. To address this problem, we devise the two-stage DSS-DST which consists of the Dual Slot Selector based on the current turn dialogue, and the Slot Value Generator based on the dialogue history. The Dual Slot Selector determines each slot whether to update slot value or to inherit the slot value from the previous turn from two aspects: (1) if there is a strong relationship between it and the current turn dialogue utterances; (2) if a slot value with high reliability can be obtained for it through the current turn dialogue. The slots selected to be updated are permitted to enter the Slot Value Generator to update values by a hybrid method, while the other slots directly inherit the values from the previous turn. Empirical results show that our method achieves 56.93%, 60.73%, and 58.04% joint accuracy on MultiWOZ 2.0, MultiWOZ 2.1, and Multi-WOZ 2.2 datasets respectively and achieves a new state-of-the-art performance with significant improvements. 1

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Baseline Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking

Guo¹,

Shuang²,

Li³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

View full text Add to dashboard Cite

show abstract

“…At each step, the new data (D i ) can contain one or multiple new domains. In addition, inspired by other lifelong learning work (Lopez-Paz and Ranzato, 2017;Zenke et al, 2017) across the same multiple domains as data of a special domain, since these cross-domain dialogues usually contain specific expressions that distinguish them from other dialogues, such as domain transformation and slot reference (Ouyang et al, 2020;Hu et al, 2020) ). The updated model should still perform well on all previous domains.…”

Section: Task Formulationmentioning

confidence: 99%

Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks

Liu¹,

Cao²,

Cao³

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

View full text Add to dashboard Cite

Dialogue state tracking (DST), which estimates user goals given a dialogue context, is an essential component of task-oriented dialogue systems. Conventional DST models are usually trained offline, which requires a fixed dataset prepared in advance. This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains. Therefore, this paper explores Domain-Lifelong Learning for Dialogue State Tracking (DLL-DST), which aims to continually train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains. To this end, we propose a novel domainlifelong learning method, called Knowledge Preservation Networks (KPN), which consists of multi-prototype enhanced retrospection and multi-strategy knowledge distillation, to solve the problems of expression diversity and combinatorial explosion in the DLL-DST task. Experimental results show that KPN effectively alleviates catastrophic forgetting and outperforms previous state-of-the-art lifelong learning methods by 4.25% and 8.27% of whole joint goal accuracy on the MultiWOZ benchmark and the SGD benchmark, respectively.

show abstract

“…It involves 7 domains and 18 slots, which form 35 domain-slot pairs. We conducted evaluations on the latest MultiWOZ 2.1 dataset [15], which 35.57 -DST Reader [17] 39.41 36.4 COMER [9] 45.72 -TRADE [8] 48.62 45.6 NADST [18] 50.52 49.0 SAS [19] 51.03 -DST-SC [20] 52.24 49.6 PRO-DST (Ours) 51.48 49.9…”

Section: Datasetsmentioning

confidence: 99%

Progressive Dialogue State Tracking for Multi-Domain Dialogue Systems

Wang

Liu

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

There are two critical observations in multi-domain dialogue state tracking (DST) ignored in most existing work. First, the number of triples (domain-slot-value) in dialogue states generally increases with the growth of dialogue turns. Second, although dialogue states are accumulating, the difference between two adjacent turns is steadily minor. To model the two observations, we propose to divide the task into two successive procedures: progressive domain-slot tracking and shrunk value prediction. Specifically, domain-slot pairs are first modeled in a multi-level structure that can be predicted progressively based on previous turns. Then, we employ a generative approach to producing dialogue values for the predicted, rather than for all possible, domain-slot pairs. This divide-and-conquer approach not only enables parallelization for predicting domain-slot pairs, but also significantly reduces the number of domain-slot candidates for value prediction. Experimental results on the MultiWOZ datasets confirm that our methodology (PRO-DST) achieves very favorable improvement over existing methods.

show abstract

SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing

Cited by 38 publications

References 19 publications

Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking

Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking

Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks

Progressive Dialogue State Tracking for Multi-Domain Dialogue Systems

Contact Info

Product

Resources

About