| Challenge: | Existing framework for a dialogue state tracking model requires an expensive manual retuning step . |
| Approach: | They propose to improve existing NBT model by removing a manual retuning step . they propose two different statistical update mechanisms to improve model performance . |
| Outcome: | The proposed model achieves competitive performance and provides a robust framework for building resource-light DST models. |
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Multi-Domain Dialogue State Tracking By Neural-Retrieval Augmentation (2022.findings-aacl)
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| Challenge: | Existing approaches for DST are conditioned on previous dialogue states, but the dependency on previous dialogs makes it difficult to prevent error propagation to subsequent turns. |
| Approach: | They propose to create a Neural Index based on dialogue context by analyzing user dialogue and previous turn state and generating a retrieval-guided generation approach. |
| Outcome: | The proposed framework retrieves dialogue context from the index built using unstructured dialogue state and structured user/system utterances. |
Neural Dialogue State Tracking with Temporally Expressive Networks (2020.findings-emnlp)
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| Challenge: | Existing models ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. |
| Approach: | They propose to combine temporal feature dependencies in spoken dialogues by using recurrent networks and probabilistic graphical models. |
| Outcome: | The proposed model improves turn-level-state prediction and state aggregation on standard datasets. |
Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles (2020.findings-emnlp)
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Carel van Niekerk, Michael Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Milica Gasic
| Challenge: | Current models for dialogue state tracking only achieve 55% accuracy . however, they lack in performance compared to belief trackers and do not produce well calibrated distributions. |
| Approach: | They propose to calibrate a model for dialogue belief trackers to measure dialogue state accuracy. |
| Outcome: | The proposed model outperforms existing models in terms of accuracy and accuracy. |
Towards LLM-driven Dialogue State Tracking (2023.emnlp-main)
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| Challenge: | emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications. |
| Approach: | They present a framework for a domain-slot instruction tuning method that allows LDST to achieve performance on par with ChatGPT. |
| Outcome: | The proposed framework performs better in zero-shot and few-shot settings than previous SOTA methods. |
Schema Encoding for Transferable Dialogue State Tracking (2022.coling-1)
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| Challenge: | Recent work has focused on deep neural models for task-oriented dialogue systems . however, the neural models require a large dataset for training and a new dataset to be trained on another domain. |
| Approach: | They propose a schema encoder for transferable dialogue state tracking to new domains . they aim to transfer the model to new datasets by encoding new schemas based on the dataset . |
| Outcome: | The proposed method improves the accuracy of the proposed model on multi-domain settings. |
Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)
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Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He
| Challenge: | Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state. |
| Approach: | They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states . |
| Outcome: | The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets. |
XL-NBT: A Cross-lingual Neural Belief Tracking Framework (D18-1)
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| Challenge: | a multi-lingual approach to training dialog systems is expensive and tedious, but it can be useful for cross-lingual support. |
| Approach: | They propose to annotate data for multiple languages and train a multi-lingual dialog system for each language. |
| Outcome: | The proposed framework bypasses the expensive human annotation and achieves promising results. |
Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation (D19-1)
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| Challenge: | Existing approaches to dialogue state tracking rely on pre-defined ontologies . however, these methods suffer from computational complexity that increases proportionally to the number of pre-determined slots. |
| Approach: | They propose a model that generates a sequence of belief states without the pre-defined ontology list. |
| Outcome: | The proposed model scales easily with the increasing number of pre-defined slots and domains and reaches the state-of-the-art performance on the multi-domain and single domain dialogue state tracking datasets. |
Efficient Dialogue State Tracking by Selectively Overwriting Memory (2020.acl-main)
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| Challenge: | Recent work in dialogue state tracking (DST) is inefficient in that it predicts dialogue state at every turn from scratch. |
| Approach: | They propose a method that selectively overwrites memory for dialogue state tracking by predicting dialogue state on each memory slot and overwriting it with new values. |
| Outcome: | The proposed model achieves state-of-the-art joint goal accuracy with 51.72% in MultiWOZ 2.0 and 53.01% in MultiWoz 2.1 in an open vocabulary-based DST setting. |
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking (2023.findings-emnlp)
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| Challenge: | Existing methods for zero-shot Dialogue State Tracking have focused on domaintransfers and have not yielded satisfactory results. |
| Approach: | They propose a new In-Context Learning method to introduce additional updating strategies in zero-shot DST by leveraging powerful Large Language Models and translating the original dialogue to JSON through semantic parsing as an intermediate state. |
| Outcome: | The proposed method outperforms existing zero-shot DST methods on MultiWOZ, showing significant improvements in JGA and slot accuracy compared to existing methods. |