| Challenge: | a global-local self-attentive dialogue state tracker estimates user goals and requests given the dialogue context . GLAD significantly improves tracking of rare states, compared to prior work . task-oriented dialogue systems can significantly reduce operating costs . |
| Approach: | They propose a global-local self-attentive dialogue state tracker which shares global-level modules with global-specific estimators for different types of dialogue states. |
| Outcome: | The proposed model outperforms previous models on the WoZ state tracking task by 3.9% and 4.8%. |
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A Sequence-to-Sequence Approach to Dialogue State Tracking (2021.acl-long)
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| Challenge: | Existing methods for dialogue state tracking are still challenging, but they are improving . a new approach to dialogue state monitoring is proposed, called Seq2Seq-DU . |
| Approach: | They propose a new dialogue state tracking module that formalizes DST as a sequence-to-sequence problem. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets in different settings. |
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing (2020.acl-main)
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| Challenge: | Existing models with excessive information are inefficient and costly . |
| Approach: | They propose to integrate a Dialogue State Tracker with Slot Attention and Slot Information Sharing to reduce redundant information’s interference and improve long dialogue context tracking. |
| Outcome: | The proposed model significantly outperforms existing models on the MultiWOZ dataset. |
Towards Universal Dialogue State Tracking (D18-1)
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| Challenge: | Existing approaches to dialogue state tracking are difficult to scale to large dialogue domains. |
| Approach: | They propose a universal dialogue state tracker that is independent of the number of values and shares parameters across all slots. |
| Outcome: | The proposed system significantly outperforms state-of-the-art approaches on two datasets. |
Improving Dialogue State Tracking by Discerning the Relevant Context (N19-1)
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| Challenge: | Dialog state tracking (DST) is used to estimate user's goals and requests in order to plan next action and respond accordingly. |
| Approach: | They propose a framework that uses the current user utterance and the most recent system utterant to determine the relevance of a system . Specifically, they use the current and most recent user . and system adverbs to determine relevance. |
| Outcome: | The proposed framework improves goal accuracy by 2.75% and 2.36% on WoZ 2.0 and Multi-WoZ restaurant domain datasets over the previous state-of-the-art GLAD model. |
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking (2021.emnlp-main)
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| Challenge: | Existing models for dialogue state tracking are based on Graph Attention Networks . if the relationship between slots and values is modelled explicitly, this can be improved . |
| Approach: | They propose a model architecture that augments GPT-2 with Graph Attention Networks to allow sequential prediction of slot values. |
| Outcome: | The proposed architecture improves performance against a strong GPT-2 baseline and with sparsely supervised training. |
DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning (2023.emnlp-main)
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| Challenge: | Existing approaches to task-oriented conversation system DST use hand-crafted templates and additional slot information to fine-tune and prompt large pre-trained language models and elicit slot values from the dialogue context. |
| Approach: | They propose a generalizable in-context tuning approach that retrieves highly relevant training examples for a given dialogue to fine-tune the model without any hand-crafted templates. |
| Outcome: | Experiments with the MultiWOZ benchmark datasets show that DiSTRICT outperforms existing approaches in various zero-shot and few-shot settings using a much smaller model. |
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking (2020.findings-emnlp)
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| Challenge: | Existing methods to track dialogue state are limited due to data sparsity and long dialogues. |
| Approach: | They propose to use the previous dialogue state and current dialogue utterance as input for DST. |
| Outcome: | The proposed approach outperforms existing methods and improves existing ones. |
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. |
Robust Dialogue State Tracking with Weak Supervision and Sparse Data (2022.tacl-1)
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Michael Heck, Nurul Lubis, Carel van Niekerk, Shutong Feng, Christian Geishauser, Hsien-Chin Lin, Milica Gašić
| Challenge: | Generalizing dialogue state tracking (DST) to new data and domains is especially challenging due to the strong reliance on abundant and fine-grained supervision during training. |
| Approach: | They propose a training strategy to build extractive DST models without the need for fine-grained manual span labels. |
| Outcome: | The proposed model improves robustness against sample sparsity, new concepts, and topics, leading to state-of-the-art performance on a range of benchmarks. |
Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues (2023.eacl-main)
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| Challenge: | Existing methods for predicting state of a conversation are limited to a few languages . a method that can be applied to other languages will benefit the large population of speakers of many other languages. |
| Approach: | They propose to automatically translate large-scale dialogue data sets in one language to produce an effective semantic parser for other languages using machine translation. |
| Outcome: | The proposed model reduces the compounding effect of translation errors without harming the accuracy in practice. |