Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition (2021.naacl-main)
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| Challenge: | Existing approaches for dialogue state tracking are mainly based on classification-based and extraction-based methods. |
| Approach: | They propose a model which incorporates both classification-based and extraction-based methods and integrates four modules to jointly extract dialogue states. |
| Outcome: | The proposed model outperforms the state-of-the-art models in multi-domain dialogues with many turns of utterances. |
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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. |
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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. |
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Scaling Multi-Domain Dialogue State Tracking via Query Reformulation (N19-2)
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| Challenge: | Using a pointer-generator network, we model the reference resolution task as a dialogue context-aware user query reformulation task. |
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MultiWOZ 2.1: A Consolidated Multi-Domain Dialogue Dataset with State Corrections and State Tracking Baselines (2020.lrec-1)
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Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyang Gao, Adarsh Kumar, Anuj Goyal, Peter Ku, Dilek Hakkani-Tur
| Challenge: | MultiWOZ 2.0 has substantial noise in dialogue state annotations and dialogue utterances . follow-up work has augmented the original dataset with user dialogue acts . |
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Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)
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| Challenge: | Existing pipeline models for task-oriented dialogue system require explicit modeling of dialogue states and hand-crafted action spaces to query domain-specific knowledge base. |
| Approach: | They propose a framework that leverages the advantages of classic pipeline and sequence-to-sequence models. |
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Diable: Efficient Dialogue State Tracking as Operations on Tables (2023.findings-acl)
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| Challenge: | Existing systems for dialogue state tracking use the full dialogue history as input and generate the entire state from scratch at each dialogue turn. |
| Approach: | They propose a task formalisation that represents the dialogue state as a table and formalises it as 'table manipulation task' they represent the dialogue as if it were a list with all the slots and generate the entire state from scratch at each dialogue turn. |
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UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues (2020.emnlp-main)
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| Challenge: | Existing approaches to training dialogue agents separately are not optimized for multi-domain task-oriented dialogues. |
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Conversational Semantic Parsing for Dialog State Tracking (2020.emnlp-main)
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Jianpeng Cheng, Devang Agrawal, Héctor Martínez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D. Williams, Hong Yu, Diarmuid Ó Séaghdha, Anders Johannsen
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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. |
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Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking (2022.acl-long)
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| Challenge: | Experimental results show that task-oriented dialogue systems have attracted growing attention and achieved substantial progress. |
| Approach: | They propose a method that dynamically selects relevant dialogue contents for each slot . they retrieve turn-level utterances and evaluate their relevance to the slot from three perspectives . |
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