Challenge: a proposed speech-based task-oriented dialogue system is built on a small embedded device . the system does not require internet connectivity because all components run locally on the device - a cost-effective solution .
Approach: They propose a spoken-language end-to-end task-oriented dialogue system for small embedded devices such as home appliances.
Outcome: The proposed system is based on a demo run offline on swiss raspberry pi . it eliminates privacy risks and eliminates server costs and latency .

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Domain-specific Attention with Distributional Signatures for Multi-Domain End-to-end Task-Oriented Dialogue (2023.findings-acl)

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Challenge: Existing methods to construct multi-domain task-oriented dialogue systems are difficult to extend to new domains due to high cost of data annotation and scarcity of labeled dialogue data.
Approach: They propose a domain attention module that uses distributional signatures to construct multi-domain dialogue systems with limited data.
Outcome: The proposed method outperforms baseline models on most metrics while keeping smaller model scale.
Domain Expert Platform for Goal-Oriented Dialog Collection (2021.eacl-demos)

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Challenge: a prerequisite for the creation of a goal-oriented neural network dialogue system is a dataset that represents typical dialogue scenarios and includes various semantic annotations.
Approach: They propose a web-based platform for collecting and writing goal-oriented dialogue samples.
Outcome: The proposed platform is language-independent and is currently being used to collect dialogue samples in Latvian .
Towards Efficient Dialogue Processing in the Emergency Response Domain (2023.acl-srw)

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Challenge: Adapters perform dialogue act classification and domain-specific slot tagging in the emergency response domain.
Approach: They propose to build a system that performs dialogue act classification and domain-specific slot tagging while being efficient, flexible and robust.
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Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning (2025.coling-main)

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Challenge: Existing end-to-end task-oriented dialog systems often encounter challenges arising from implicit information, coreference, and the presence of noisy and irrelevant data within the dialog context.
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End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)

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Challenge: End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity.
Approach: They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends.
Outcome: The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity.
Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System (2023.findings-acl)

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Challenge: Recent work on end-to-end dialogue models with pre-trained dialogue corpora shows promising performance in the conversational system.
Approach: They propose an end-to-end TOD system with task-optimized adapters which learn independently per task adding only small number of parameters after fixed layers of pre-trained network.
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End-to-End Task-Oriented Dialogue Systems Based on Schema (2023.findings-acl)

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Challenge: Existing approaches for task-oriented dialogue systems rely on a unified schema across domains, but we propose a schema-aware model for task oriented dialogues based on 'slots'
Approach: They propose a schema-aware end-to-end neural network model for handling task-oriented dialogues based on a dynamic set of slots within a unified schema.
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End-to-End Learning of Task-Oriented Dialogs (N18-4)

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Challenge: Dissertation addresses the limitations of conventional task-oriented dialog systems . conventions of such systems include a complex pipeline and dialog state tracking .
Approach: They propose a neural network based dialog system that can robustly track dialog state . they propose offline training and online interactive learning methods to improve efficiency .
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A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots (2022.findings-acl)

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Challenge: Sub-Slot based task-oriented dialogs provide slot values segment by segment over multiple turns.
Approach: They define a task called Sub-Slot based Task-Oriented Dialog (SSTOD) they build a Chinese dialog dataset SSD for boosting research on SSTOD.
Outcome: The proposed task is called Sub-Slot based Task-Oriented Dialog (SSTOD) it includes 40K dialogs and 500K utterances from Chinese names, phone numbers, ID numbers and license plate numbers . the dataset is well annotated with sub-slot values, slot values, dialog states and actions .
Addressing Domain Changes in Task-oriented Conversational Agents through Dialogue Adaptation (2023.eacl-srw)

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Challenge: Recent task-oriented dialogue systems are trained on annotated dialogues, but when domain knowledge changes, the initial model may become obsolete.
Approach: They propose to use an annotated dialogue dataset to train a dialogue model for domain changes . they propose to fine-tune a generative language model on domain changes to reduce performance .
Outcome: The proposed approach reduces performance by 55% by fine-tuning a generative language model on domain changes.

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